{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "#补上Y=WX+b\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside `cs231n/classifiers/linear_svm.py`. \n",
    "\n",
    "As you can see, we have prefilled the function `svm_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.223676\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 11.540325 analytic: 11.540325, relative error: 1.912016e-11\n",
      "numerical: -17.344594 analytic: -17.344594, relative error: 2.785704e-12\n",
      "numerical: 23.401471 analytic: 23.401471, relative error: 9.241485e-12\n",
      "numerical: 5.979763 analytic: 5.979763, relative error: 5.746133e-12\n",
      "numerical: -25.476529 analytic: -25.476529, relative error: 2.027819e-12\n",
      "numerical: 6.546028 analytic: 6.546028, relative error: 1.698739e-11\n",
      "numerical: 30.952135 analytic: 30.952135, relative error: 1.181727e-12\n",
      "numerical: -2.294217 analytic: -2.392229, relative error: 2.091402e-02\n",
      "numerical: -0.993583 analytic: -0.993583, relative error: 2.017725e-10\n",
      "numerical: -10.290414 analytic: -10.372839, relative error: 3.988987e-03\n",
      "numerical: -6.875032 analytic: -6.875032, relative error: 2.337365e-11\n",
      "numerical: -1.799692 analytic: -1.690202, relative error: 3.137368e-02\n",
      "numerical: 1.404513 analytic: 1.404513, relative error: 1.384576e-10\n",
      "numerical: 14.379494 analytic: 14.379494, relative error: 1.511828e-11\n",
      "numerical: -7.656742 analytic: -7.656742, relative error: 1.658236e-11\n",
      "numerical: -26.048971 analytic: -26.048971, relative error: 3.492912e-12\n",
      "numerical: -1.673034 analytic: -1.673034, relative error: 4.668217e-11\n",
      "numerical: -1.449063 analytic: -1.564592, relative error: 3.833509e-02\n",
      "numerical: -23.135826 analytic: -23.088241, relative error: 1.029450e-03\n",
      "numerical: 6.012284 analytic: 6.012284, relative error: 6.280500e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "vectorized_time_1",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.223676e+00 computed in 0.135638s\n",
      "Vectorized loss: 9.223676e+00 computed in 0.003991s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1],\n",
       "       [0, 1, 1],\n",
       "       [0, 1, 0]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array([[1,1,1],[-1,2,3],[-1,4,-9]])\n",
    "counts=(a>0).astype(int)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "vectorized_time_2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.133670s\n",
      "Vectorized loss and gradient: computed in 0.000000s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss. Your code for this part will be written inside `cs231n/classifiers/linear_classifier.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "sgd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 399.602494\n",
      "iteration 100 / 1500: loss 236.690658\n",
      "iteration 200 / 1500: loss 144.456391\n",
      "iteration 300 / 1500: loss 89.184433\n",
      "iteration 400 / 1500: loss 55.880135\n",
      "iteration 500 / 1500: loss 35.075458\n",
      "iteration 600 / 1500: loss 23.375377\n",
      "iteration 700 / 1500: loss 16.331849\n",
      "iteration 800 / 1500: loss 11.388991\n",
      "iteration 900 / 1500: loss 8.702171\n",
      "iteration 1000 / 1500: loss 7.686279\n",
      "iteration 1100 / 1500: loss 6.442895\n",
      "iteration 1200 / 1500: loss 6.005316\n",
      "iteration 1300 / 1500: loss 5.639037\n",
      "iteration 1400 / 1500: loss 5.616634\n",
      "That took 7.244173s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "validate"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.099976\n",
      "validation accuracy: 0.102382\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tuning",
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 405.785934\n",
      "iteration 100 / 1500: loss 239.974650\n",
      "iteration 200 / 1500: loss 145.640967\n",
      "iteration 300 / 1500: loss 89.607073\n",
      "iteration 400 / 1500: loss 56.043374\n",
      "iteration 500 / 1500: loss 35.317953\n",
      "iteration 600 / 1500: loss 23.544510\n",
      "iteration 700 / 1500: loss 15.831411\n",
      "iteration 800 / 1500: loss 11.759424\n",
      "iteration 900 / 1500: loss 9.048581\n",
      "iteration 1000 / 1500: loss 7.400916\n",
      "iteration 1100 / 1500: loss 6.694373\n",
      "iteration 1200 / 1500: loss 6.613429\n",
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     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "# Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "# Provided as a reference. You may or may not want to change these hyperparameters\n",
    "learning_rates = [1e-7, 5e-6,5e-7,1e-6]\n",
    "regularization_strengths = [2.5e4,5e4,3e4]\n",
    "\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "for i in range(len(learning_rates)):\n",
    "    for j in range(len(regularization_strengths)):\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train, y_train, learning_rate=learning_rates[i], reg=regularization_strengths[j],\n",
    "                      num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        Train_accuaracy=np.mean(y_train_pred==y_train)\n",
    "        Val_accuaracy=np.mean(y_val_pred==y_val)\n",
    "        results[(learning_rates[i],regularization_strengths[j])]=(Train_accuaracy,Val_accuaracy)\n",
    "        if Val_accuaracy>best_val:\n",
    "            best_val=Val_accuaracy\n",
    "            best_svm=svm\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "import pdb\n",
    "\n",
    "# pdb.set_trace()\n",
    "\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.tight_layout(pad=3)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "test"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.371000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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ZQ2AgtcH2Ojr4tM5LCAtcWOz8p0tSV1eZgXtFyrYLH01tFNea8BdNtnHDJ1+mnofGzj4AIF3gBmuWTwEAc7XNz3JhStVT8Fy2/3KH7c8e8nnlGV9+MB/gzhXlx3VpbuoVWZluc8Zrm6aPaVab+SX5Gyz5f1IbZT+jDS68jcodLohX7rPJQz8ZxTXWeY+y7S7O8STBMazLjJefUJ42dTCfKNgYz7lR5wLKzOIegYCf4P+xJedMqZ/D0TbbF1vR3JmZcROabpIn6WwauUvOn903ODblLJ0KEt0nAIAnxoJb4TObM5pLZy7HAQvyKplm+5Z+Arkcn7M6Wj23/5EJJaKIIoroJaUXe4iZ5WFC4GtntKqwpH5nKlIrpKZKi8ak/zpu36ZaMhvp0FIqshejShmm6fpzu3EJLPcBAE6WalPoES04Rb4ztQKqGapSyyU1gazQ3XKDCCB5coJFiX/ndfgSS8hFKsG2rIRIroyFhcwYXrqxPlO6fLefImpIF8fwj2VqCohoC0n2N9/iNePBYyzqRAGTFA8SnR2prJdEUHfndIu6OLnG2VCIu87fEudEH6V9ou1VrgA7wWv8FbWdC5fuiDt6fjofR1YulV6X79ouCR04RD/51Ak/hiUsTh+r7c8/hPk4svLUdhyHWoBdK2AwJQ+6FzIfcBgwr+r5l2MMff1tpA5/kbwtO/sAgKJFjS2+quAioCwe9Yl6bqWJoBJL8vy8BtSS/K0tefU6cgkDeWWFM6QMnxOXGnyWI8pKFsjbsKvD5OwUr+qg6+n1pzvE9HbJj9KE/TvI5HClM+KhNM3CB2qb4Zxwtpo4vWIbt4q8eLYkunv4AfvT7PFwOxMssfMq+3p4RhnJL3jNdY7I9Ha9gUuNxWwuV8UxEeqpvq9kG8BCmqoQcvU++ZKe83A8NqRmFzY9eAMeKGbi6y9HqQKR7nmabSh6LSQLlMdqwPaFNWmT0uqrmSV25GJ4eiQNyqOWldHhbWauQ0x7jrTMSQ1pxKizT5dJrkeTKxfpGJ8zlDZTGui5Ja4be6chrhL87ajKvs/laluZ7gMAem+yTebkAOki15LwZy+f2/8IgUcUUUQRvaT0QhH4KE7kUVkpWCeYom0R8eymiSh9jzapeYkooZZdYXhwBABwQu6EqxZ3pe497mAmQTt19V0f2W3uST2b78qkiCAWYx64DKwBiksd+NRpfzay9/XeJ/KzqivkMwr66HJXnO4J/R4T4RTK3N1zbhm5FW2Jqc76h5ivpWWvBRFOsuh9LxjASiqAJMMd+/Gc/S1a+yjk+M7DKbWIjUvu2IeXtBsb2eeudmKonrO/3eArAIBXN4hi+7YOSK7jcJt85530udrDz5MEef0wKGHnmuM2lv3y3QER960hr02M+NwTc46mAkH6E2dtnpzJlh5UOHbZShZDnU9Ublg8Jf8nstn28zEkn7DPUx0+ZvqE6daAYzmIcQyTtQCtEd/h5NjOREDUttyUnXQDWAopWz3KRWfO5yUJTrGV3MZTQzmdyGaaPuLntzmcCLZof20/fYjRHtvVyPyRtXkCAEVDBDcRUm0eB8jX+L6FgkOcGJGoW2MD4oU2wg7HLZxx3NwrHVTaCparsl9nmOO4twcAyBr2vQMiU/uS6P08TKBc5JyIbUmTPlnoXdTeim4X3TbHJVcXknQ4v4/j0nodabhn2+iMjgAANf97RbM+MZkp+5Kt6SB1biO+IJ9nefarELD/uRzn0TK9Qq8tjTNGmQn67EMjRb71s3J/XGzDDslbX+7LhwqIytn83uu7KNxkke1wPMZV1gyZpyinVnMDBQVMxXWWNFmwzWda84wOtzfCBgIFqk01174fRQg8oogiiuglpReKwPty6r8eEKltF/KIDbgLXapkpXWbn1tCP4/T+6jtyWH+Jsy7yGCTkuGul9c+1P/6FANuinDkIziqEJ0XSzzxLQ/yqA7Zjplc50bXRAepGE/VTycpJIw0gpqQREu29BMiinZCLj+zCyRf53Pm8d7aPJnuKjRcaLG8XMJRiPpPJeg14YzZl1WZ/Rx6faSfEhnNDHfzJwoOmW8SSa4swsSls8BIXifLBtHB+2PyM7MiqrpKh3jlhGjqO6CduJKjrfJUJ/mW76A7JQLZtYh6hgui2JQCoRYTIp03rDEebfO35sP1PQtyGtdFmuMUrgx8hcO7Ux2O3GY7HWlI8QsX/i7RiiXPmfYBkdJFnprC/jafu7loYtyibT+cE/3EN4k86wWeHSytA/gKfDl3+X9WdtLphLz+0J4gNyFPnSLtzK0sZcHEOWbeNc8LNvcacAK59rmfzgvFz3Os8wk+c5yYYVAj7+0r8vnC1lh41JwSp0UUcAgA6MUpPzFbZx+aNzF51Wyf1XF5W+cYCnCy5NXU19i7hTgOekSKOws+J9jnnCguOSbT0gp5LS3XE45P0FInOpTP8h7viT31EWzyOQfXz0ebH8uTG41sRI30aDJFUik27sX5rnmBvBjJBTjtD5GUdmfJo2rvhO09aAjTKigsmHooFihzM7zP+wNpW0PJTKOL4PCmRZQnz+V8Tp1xrvWKGZTkcRbLUJNOT9hfI3nYGLHdnbdbuH3Cdado7j23/xECjyiiiCJ6SemFIvD9OHf3dk8nvG4C7Qwh89aKyKU05s6zVGDE7vgI3SKRnTuXz+85dzDrLhHkhRD12LUQl914ppD6O22ihQuPKCpvWpjJGyYtP994/ggAYE/YhnQYIGlxt5zm+Zxqn++E7PhhjM/I3i4gocRB5fMbmPHJafNS7d0QAq80UJetzl6xX4fasTNLXhvLzHDqcrcO5Q0TKIgomBIR+grIaVfuwegkPHPCnd6JEVHEYuTJtRfH1kx29yZttktHAS7v8Tl2aghT4LP/z/f425dLRILDCq+ZVqmBZKchvpJlcp/H1ffX5slYNXYrJbZzMIqj3uTYvzunjfeVD6mFGSXzmlTLyM7ll9uiDLWVbClrb+h/2cCnF1gU+NudGdturXiPb2TPvXYw0/mJ2ZFW8wERpvGJdmOTEYYlIi47Tx7n+pTViwafs9rgPTVnF5UW+T1afLoUzokO0fVhVrbskySK8ki56FHDXBTZtmzId90u2HhXXkixnjQoedHMUkT0oyHlN16w0L8kP+ot+ckXKHspn/8vYo+wXFHWzoX/3hzyOR+WydOtzg4SC4WFv8L2eeNXyQ+NXweU9/r2E+T61IaWSpWxDmVyHPPTEdu36hYwvce+n+lcotikp4oJ+O7jWhM7Y8pus8G+n5cV5DXh8+IKuhq0isgoOHCpdAJ5eaMkhPAtx0fhDaU5cNiHk0CHIHGl7Zgc4SLJd+y/z+dcvyHtdsj/e3t8RvDuAl2F+idTD5/b/xe6gLsDTur5Fidd2DvDpvIBOBaN/imP7lyDS6nI21vYGPLvTIqLy1WdzZ4suKCEZUWJHdYRhBTkmNwRj5UXoRhTLpTsE8QrEsoxF61jBbp8fclJWC1dIZ6jUK1O2S4vS2EN4sz7UY5zMk06Ia5TnBC1L/TX5snZkoJelktSsXgfYZsDnY1TEP0xfzvb5GRIfLjAOM/MghlPC5gW4FGan0cBhWTe28Dim7rmdUVr9tnOtLcPAGhOe/gtmR12QpqyijKPKJ4HJ8U5bp+Qz68bmbKGXCzSkHkqQfNDMe9jruyPmfXTW6AVsv9LHdouLBdWku17TZkQY7coJwW/oLZYcBQyafv8rVpjHzbjivbVIjRsuSiecfzGebnWPeQkLW9yk/PLJXgpTr7lQ5pi/CY3gkpT3x/YGMu0s5OmDJ1qI1h+wM+v7bC2dqK0gHtFWYoXd9ZnCgBoQw9PdThbnKNfJz9aGf0m97WN1D4AwPtiDq895qJyefktAEBvwt+S4PhZishcOGfYNQwyeaVCs8N34mx/+g4XukW/if2Conx1EPkox2s3HbkI1idoaoE8S3FOVW6RL6dPCd4yckW9jlVgOQJO5dfWZom3xbFYFbUA17pY9Sj7VkaH6zPK7UTux81hDUYZAFNHBGSFEn8rnmtzUp6kJhzk5Aobk73GW2jDa5AP7ngb2Sk35SuBr2DI8a+mOPcuXYOyMl9eVAgqg5wCE3fIx2BKE11jJ8S4RzfeZfr5pXkjE0pEEUUU0UtKLxSB97SrVRVWe+2tUPGFWJrKX3GTA3qHu9SW28ZxWShHrm21rkK369yxKivuek71EP0Sw5WLp0SSVzk+x01x94vNMhh2iGiNshx+QdnaeiAaa1wvsEprJ01wJ52P+b+rnd5c6jMSSMllsZ1YP5dD9h537sRNLhMc4yKpcN4Y2zCrsr/1C6KhwayNog5Rn+YVsDQhL4ZjBd7Ive5J/CGSNrUR/wn5b8QTeTXhW14beyDfPIeIJqkgiKnDvjUyY0xmRKKNkP20t/gAb0VzxnaMz18t+3AUqDEKnx8K/HHk3F7qOWxnc5HHUIeDXox8uopz7LIaH9eaoeGzD5M9ytDu73Ksgppcua7IE/vVFSYV8iT1RFkNR3SfDFMcj5hdxEqahvc2UVB4pUx6SpkQ3h2j1ud3C7MPAOgkiUbvjcjH82v+njwvoSS0XFZOjnVpapMf/RnHulnbQm0pJ4CJXAuVfxtyp612XTiGyDGRbqmNlJ20NLuq1P2n2RqKNufAe8omWLa/zXfP+Nz9RhqP5jpQVmBSs0+Nwmtw3GJBBmcWZeQkoJacvCRfkzLb3JgvT8tJZCuUMU8IdB26LPGet6fk7RhprBZ8h7vUYW/qOwCAvFxtrVgXQUWmJlCTTYXvAQDsLWrT4wW1uHi7gw/U5lZac2SofN6b5Gd65KGd5neFqcy5GbZLFi7k8iXEtKb0t/lbS4FQvbm0QGXNtDP78HPMIx8khs/tf4TAI4ooooheUnqhCDyZ4E4TxLjL7WczWOWJ6hZ9uYDt0wZpjXUYl87jloIIxhbtSfMNBdPEFcQwIAp7NxVDfcYQ7jOLz63kafesl4kyFrNL5AcZ3ccdut0iWq/ucIedDmawXdory3G6Cl0maedtDrkzDz2ihkUyQEqxKvXR+oEIt2lGQ1wHZaWTCkZZbtvXIXfs+8qz/ahN3uQ6eYxSROXpONGPs0FepHQQlHB53rC1TOFUgQjVOhHD0zF/KyZ47e3p21glaXOLhQpM6bBToc1rwmAH0wTR5PKSCCKQe9ZClYPMrgJmullUbSK4qbc+2rzsUNPaMhzDc5TRmJIXT+SuV5bte7jiWBbmaXiynRdnbIfz02xn6kKpGIY6dOrVURMCR52/ObUjfrZVlWl6AlNj8Fe5rSRdOcXvFxLqGwCf1+QmlFHbJhI81MHXHfBcwNq9QjJgHxaz6do8AYCNGDWBhWyyxeEVYkXalMN9Hhbn2uTZVPmkp7aNtsYieKQMnyXe8yTNawaqSJUvnuPUZ3vLFuV+pXQJK1W4uvb6SC3J85iS7pcUEDd0lPLBNDASkt1ccp7YCZ5fLWIU+KVSD2RXC4QO+RnLPFibJ1+cckxnSmR2OrewoURi0w6fG17oDK3J/ubLCyR1uDjSfMkEROLmgikIMj7nzOx1g18Yq6KVtL6ZtGac8XlWboxtR1WJbPKyoCyEng7+3aAL/5xjE8+QJ13VI3ZavDctS4AJAxSUX779A7IRRgg8oogiiuglpReKwDOGdsaV4qFT3gCuEJSV5u7W63HH+pkMd7BvriawN4iWlnMis8wF0cZctt1pksi5kr6DckdoUwh1nFDNQoVn56Y+oJSbSyU9yiuhlt1RjcfZDvyAO7E3k1fLKZ9rKQ94SZEJo1yIyh5toq4Cg9ahC7l41YvKKV79EMsT2rG3XyNKOZanypZslgc7e4jvKg3AEa/NqOrOskFkEfo6VS81EJ8rbeiY6PBrCjU+StG+NrHb2L+pszijnbiQIaqeqVZpPFgh2Safwx0imrRcBe+nyevBTKHLWcBpM41mfnRTAfWTU9OwLX2FLm+nXHTqRCJl2eF9aWGTq5vUCwO8ArZn4inkXWm3R0vVi0xRU0utjnHwe/+Y340VUn9bwShD9n9sJ9G9pv03W+b5ia8xgkWeVFpvwFJagosjfleyKG+D+0Tgrmorpq0EfAVfTbfWZgkAIBTKyyi/tV+rIRuja26zS8T83gHtvTmLspO7fw7vSgEmyWCKAAAgAElEQVQ7VaLgcY6yXL4kL+e2kn/V7mDhUn7GCn4KJ0L4X+XcmPeWyJeZPjY3oxycqXJSs0r+W/Ek8hnOn1mcniWdY/Kn3eJzkrsKPT9LY6hUB3uJV9fmiaP82wtDWbYrZeTm1J4fvcm2vyWGlxWYNUy0kNqixtS8lDZRIQ+Wrs68QqV57hVwNJJrqXNTeUhpp4/p1dMu7MKV5laXW2lGWvMyzXtqFykkCkcAgANQnlLSTucdyoWvil7n9QwqI45xrdJ8bv8jBB5RRBFF9JLSC0Xg+ZwSBp1z5/aRQCXPnbOounznF9xTjt+Sd4pJY3ggFLPNXfOkTrSxKfvVUJ4szuW34avIwBsCw0/nCknNC0VNXYRjooNcmbtmf0Wo9pUud+jTsx62d3jfSUFI9j7b3lMYupnQlpcILXgfcpdsbKzPzhsTl7VPLWDUu4OeaivsDW48QWifuy4x1P+tRA7hMe2hLRUQuLRkR1MhBeX+x8Wkhz0FxLTn7EOywN09N1d4tH8XLTCl6DRDlBa3eM1uSgnqezNU79DuXFIysltK+3rpk9n2hBpWZ2yhLhRd3Fo/kGdu0xaaM7Qvj7MXCLuCreJXMs7P21nKTWBt4pEqD+0cc1wdl3JzOqTdPKdzkYvrHtoOx7q/4m/2P6YseRsK9nlzD94x/w6a5O04RlR2K1RNyKKF7EMiSU8C5x7xXKA+PgIANLIcpyBVQv418nI0Xj/lAgA8kPdJQVV+/F4Bj5L0va7H+D67RL70Y0K64zqMNJBegfxZavy8uoKQVncBAKtbARKPed9NbVQojYB7vKnnArEc27FSWoOCtKJMlTJTrR7hkUe+JLs8t3FjlI2WqyRpR3pux0eiIm+RmzQJa1CqT02sIw+k+OgEZ9LMX+2xL46CdC5S5FV+egrvd9nmkEsJxo8UKwGtO8rT609XSKb5t6e1JExzjZm2OGfjbh94Ql7OJtJCE+yfSZK3brqNcLAtnnD9WbyhRF9GKapXlLfdeB5VFXfo48lz+x8h8Igiiiiil5ReKAJPBPJZzSqpT8lCX0loyltEBduqTXeuk/+aKcDb4W6UDFSBW8meYr4qgpd5z6ujOt6P0WY9uknxqlSzBYVVe8kMygRCWJ7yOTVFPF43iKJy1QXaQ+6opQuhHlsloFTYoZ0lai+vUsi8yVPlofP8E+OP5ckb0h6UHsAsMth/ynYd7hMxZOVn/nqbOz82t7DYISpYGpWgm9MuZ+Q9ElOI8aoSRyDPhL2SiiHMlMzeV9moxBJlo3JfilhNZFmaLmaIEjKWg7psfxuOfM6TbNfGmDb6YVWh67ELfGdMbWF3Wl6bJ02fttChvJaynRLmcaL9RxMi7m2FGG9liF4ePs7CL/P6b+r/9E1E6xXHaqj0AOOYg4Kncm1Tys7liH2JK0VBOlVFV2cjqlaGlDySjjPksfWdGRJKU5BsK3JYKWt7C8rdTbTjrNzDckUUmnQ/XUGH0oL9Gah/o3kKroqTeE35L7+vAhJFqnEX+TFWoOw6ccVYTIX0MtLMCoqjOMxgvqXkVTPJd0VFBpTOougmMA2JVt2SwuFDtuG6yuePB03kR/zu6IxtjSmla6gUGYslx3MztoWnrlyxCudr88Sv8j17eXpIfTuZxnaGPFgoEnPnSqHwSWqZM6+IQHVFF1NqCCeKpFwMKQ8pj/NyVQ7hPGS7piXOqaps9gklLGsvUwj3OP72ijyNV7mWZFzee1azEcaoCcUanD/mSPxTHZipapYm4wN4Tf7WWL7y3P6/0AXc8SjMt/XaWHGFlKq83ESVfClPNeNKFTaSPQ9z5YIeKtjCzmhG9RTcodwQg9tF3O/sAwCOQwrwa8rj3Uvw88ZyhulMOQjAw5zkHge8e1P81imida2w502F+k94T8PoQEyBE8eNGbKPOALDVHttnsRdCfSFcgLHlvigzAX3lTOmOKsX2b+krbDs+DmOlypI2+M7m6qh56d00KVQ5USqAFf5ngsK+ijEKcjZUPk/nAxyAa/PSj22kioy/R2p61sLKCoYw7uc3InHnCCucowkNB7IVfF2hVnUzvrrF/Adq1pO0uHiMVx5CHNcPCtJTvZxm+8ay43OKtnIJTihChPK1DsLbiJN5Tu5Djje/c0Ssh/y77QCbk6UrqFpMYCiX7lCvExZnIzYh9W+qgKdqJh2uoBBXOZAQzkJb5Nv4fsK/lDFnuwBsHqD18wu52vzBABCn4uhu6R5ys0Pv5fX5Oz4ZuNWceo2eTFcnWFT7mp5gRBvh5vOoz4X7mttWnnPwWLAjTyW5gLnVCkPtWO2/eyOA+9CeemXXJSvupSn2jW/9+4X8KFC+n2lIygk5I4YUzDVF9iWp9+YIHFFWUlsPF2bJ7bPPnxY5oZ5b5jDNMU5mpockRcZuYNKTi88F0nV0X33Q7nL6hC0kSb/JqrMlOm2YZKca1WZvqZGqTtW5H2/6OD2Ifs+3uHa1LApcxdKcfAzYxfv7dKcFlsJLNym6dXJch7Zyr2eyVURU03bBzLffD+KTCgRRRRRRC8pvVAEPrO4U5+OuRvvHLtQNC5iA+5Ortx54gF3qSdbFpIp7papfe6syVMis7MYUXL/9/mQ1muAp0oa7pjv6l8TmWZAFB+EcyT2ucuGQuULcPfcSutgy3mMfsAdvbXgO1dyzL+44VhFyZqWPoZyuRot9tbmScqlCmiH3H0XyxrKcplTOU5M6tzVazqomlW2kNSB6x2PbXelhjaVuMm5QyT5BT+PxStEDLUs1bvugoeZo+/wwPJ2wkZ5h/waz9mO9Fi1/VQxfP7BEa5VKcjucGyyoRCEDg/rU7bbcTfg3mY7tsvrV+RxJ2zfTcbH2XYeUN3GhWGbc3Eh3bLcKUfH8AzbM/N5kLg3I1I9TxFtvyYN8OA9F85NTnQdNhWVHzyl/OqtYh0TCac3Zb8X76gKkFTzoHmMICSabb7CawvfJI8t8TPsyp0s38PqjEi4Ovx0uKm9pRqhZ+xz0F1hXiMKTCnQZh6n0Iyy7HMwy2MR43v9QDU1LfbRl+mkqMrzSFlY+ZSjQPm/g4eUh8e71OLeWlzhsEre7z0kHzo2Uft1g7zYujDInnD+jELOjUlbNTCVMC75Ll0RZ+WHeE057N3B+lkalx55UVBCrEnlAJsFyn5P7paPlOjtrg6uE/0FrCllpDIm/ypKtXEts+Km0gUsUn0UZ5RDa1sufTLv3mppPsxzmN3i9akz1cKsMDTf6TKn/8COY6tP7eNYdXXDDaLrzR7nzaTINiT9S3hN8n1D5p/vRxECjyiiiCJ6SemFIvDTY9q9Ugpv787PMD7TTnNHIayX3HG6Oe52SW8Io8OzQYz2zmKd6Kkyp6tToSr0aRu8d8xdTbejoOopB/NHAIDtbAn779AWdq1EULZDdOBtEj3llxeoFhXmWuY7bxJorRSyG7ypQ53rIoZXSgupg5l1yNaBxoVPBFhCgI22ckkfsJ1zHfq6hju+53RQWe4DANI+0Xk7xXZuqlz7oyRtmLmtGWpPqLG8L3SZBRHExoL37m2t4DH+A6n7qlxf1SGOShzlzBTLDe73OVV9Wd6gPYUIP5W9dy+ZxnihQ9bF7bV50k8KtSvh0asrF+0NorxcTIfICjAqFzhOp8lXsIopVYPcyJDgmUllwvF9eEW0thpaKO7xeRs3gSYWXReN3OcSoyRmSt+amMoOmSdSvW/I66O2h0qNqN6nuR1J5UTvK5lT1eX4OnOgrKChUau+Nk8AIO/y2eOREHhhAldyOlbKg9k5ZXE45jv6uRG2mjwU9kMGJg3PFZKvCj0znSsUqxn4SuF7g+B3QlXm0eez4xLSQv0PS3x3TlV77AWfO/QPUdMh3tUJebaheqDJET+vqmxLws3huw416Ya/vhvhparBJwrUzJyZj1FA2a36XCfsO8oP/1jBffE0bPFt1+bc8MHDTLtPmQ6UIjrADJOq3CW7bGdGiaq8s30AQLzYReNUdTIrPHTsqD5sOkUZfGKa8JZKk6wD3e0htft2g23PX8u1cpnFcsC55HhRKH1EEUUU0eeSXigCL9jccSYOd6B8foTU/X0AwEg24Lmqree63PEv7niwbk7U5UpmKzHVpKTQ6IA75NXjE2RntL35StXYVrGGlNDG4fQKvQK/s5Qq9bZLNNZTms9looIPG/QA2ZUr2dLi7uku+O7llDt0czbCYVw18uLre6HELHqh+BOhg7MNNIRongioFc+P+G6bfLjb2oHZU1DBIyKuy/dVZ+819ik1l5vccB/eTaKqcyLwm4h/f8m+PR2v0MorpPyatsGWXDRducX5dRuWkn/NVqqMNP4dAMBwRlS8SvP/E3eFjCojbaXM2jypnCgN7FfZ0CfjOHI+bfGrls44JrL9XxJlbWCJgTQD51xJz46IrhyPyHUeF2r/8gj5vooY5Nm/DZsoKKnxtr5aQUW5lc6Nihmo+EMjzuCMZqaNgtIAexuUAUdumEnZVhPK7joLAuRS1DKDq2BtngDArCNvrKQKmWRTCI9Y5f1SY1lJcPxrCgWf2z6Ohwymiifphrubkn1+TgELcvw/ORji/oqIL2YL+Rm2dTnkeUL8zgWCrs4PlH45pkozgarthGaCiaFmsvM1ajg7SiT2YExPk2WPMp7yVrg15zu6mfXdK2N9PjcVyu0176K14NiuVJV+5RAFb+/LffbgAHWXmtY4TqQ8jVN2a0t+rqaUTvY4h3mT83p+xLGuqoiEJ9TuXpYxsjl/87d4/25J1bQWKiIBG/Uqv+uIp3MVnri9ocRvW+z/2J+gqICn44vvFdv8+P4/99eIIoooooh+YunFppNdcJfa1I6TCF5HTAldpq4qp5dpR9tVhfjxcBOlbaLCW0naiV0VOlhOFCo7VTmyRA5hSoENK363UG3LvOHuOx0OkJVvcldh/B9uqTyXak5WVys4R/QoSRb5nO6Uu+5uklqEf67ERAsbaQXRPArWz1KUvglrloeBgwnchxwWFcrGlrx2Al+BAE4NrW8SBY8mqu8ZEEH4h9RGkntCJtYlrp7KHi0PACtLW95UqU/3OgHOLSLwHdXW7AoMJZIKgPKKGMeP+NsVUZQ/Uak38BpbSbIC6wqNFvm1aKyPwN0M25mQJ1JuBSRV63E6U31QHc4bn9f0ZgaxNse+r0RA85kQYVYxAUqGdn4+wFJh7aFNrW6RIC9qMWlajgP3Hsc1o9iCwhXffa2ydbGWgSUNZXXO/rr3xDih9oTF8fj6hYNxUqHTzU9RZw7AMsZ+JQ09O4aug1MlRgpXHL86+L6OPHhayKHpsP8ZaaMXHmVuumBbX9XZiFuMIybkGJ+zrSWlTW4viTarV3XEZN8eTPhdXMVAWmmi9OHJE6wKfOatYB8A0FXsRkZpDRZztqmb7yM1Jl+TZXttnpQlKzGdT5RGWcQqRMyDmVJGKM3tteILGoki5ml5tMU57pszlXRUXc8wVJBbKo2LPvtbrnDOx6ts74ViQ5IVAzuQfMYLeiflaE8aUTvtoVvkGGVPeY1fpDxOn+icpEmZKfTTOMnx2kry+QVRXugCrnrACGNkaLkU4HzICVBWLudM7GbSkWn3kgbnQwV0DOQ6llQR4ftkYCzOwRueXOBcuS0GEx3QaGCqBaq9uVwK397n/TsHqs93yDakVPfS2Bns3FfUlUwJm3c4QMcHyiSmw8zrtItSgov9q5mLtXmSVs3JwgU3lWW+iKkOLTcvZQZR5KQZc1K1mj4cZXXrjymIwwzvmS844PlrBbzkC5iU2GYvK4GzdHCjXBFW7AJTHUgVuhSm92WCSijv9qL7FKcWvwuVZ7mjA66M4SRa3pUqnV9hE9yInfn6kZiLHCd/+YRjMC8vEeuRF9Y9tiE95bu6OqzrFlZIOuRFoc2FZHWf1+QOOJEP5DJYSBnUNS/8E/LCilMGOsqFErscoqj827m3yZuVccUTAojcLId+S3ncF3xHsa8gM+UD90Oab87yOQxVE7M0+nRFjdvH5MFEm9bKSqBR52LuFbkh1RZs/6ywDwDIz0KkVAHGVXBWKqCc523eu1QlqVxgI7vitadZFSV2CJoaA4KoUrqCjrI0tsosFrwhRd6N6wB3Eocnc9a8yMXUel8BdRVFqGZoargfbOLIYb6P6qK2Nk/SJfbJlhlvnBqir0xArjKSBlkV/JYZZ1QP0TiVKe6m6lUo8FamvNdOuBFelgP458rfPeK1vgrF3k+SVw+8CZK32N9Nn2P0oRwEJrvypshbKGj8ThVJXlTGSGNzQ78Bc5PiAneUC3+QSD63/5EJJaKIIoroJaUXisDnlqq3qyDJqHuJuqpkxIUc0lKRU3XuTumUg+n0pjo5D2wqngJuelKNVoRT3eQQlwG7FKjaTCFGhNVocPe87AFbF0T3DY/vzJa4w1oxvscKZ/B6qsuow61hyAOoqoKKVkI1dx7FcO2xXavl+vth1iYiWSkz4uhpByeW0L1KBO412SdnzmpD2cnbME/l7pdiGHJlixf3MkTevnI0rzwH7Z7yysyIEnLFb/BaofVJLY7sEdXg31b1HsuXSavDvNlzawPTEU0xkz2iigw1aFiqn5hW7u9lOYFSkW3OfWF9s1I4IWJ6UKbr54bZxyoh97OnHM96RRqCp0AW/wJ+nH8vKkTeJbl/2XJHDD2iYjOK43xGtNgcUsMa2bz3Jo9zLF1H/jb/VjpwxJa89rCkoJ1BAQ1pkIkWZWjS5jsWMR6SmbIyX04/QF6HfyoQtDatljzQsrNEkt4dG1CGu5LcbzN7ygz5PVe3ERoNhXUfS/O02KHLMtsTV+bI0rQNV/UxX1cOF6OQ+skWx3E+ncNa/jwAYJmhmeFywOd7SfLHaiSwccr3d23KwS3l5c955MeOXAYniQA5ixpJz39+2PjHUWZEGXmkepVvZy6x9Pnu8VLV45X8KN2hBrrMLr6XQyU+UZ75tPLqX3NxmnxFps0nCezp8PK4StPhMEuefKiDdNNoYalUCt+URmwalKfsTObidhLje9QWsjf2ySTbPnD3AQBuqHqhd3fQa3Osp8oz/v0oQuARRRRRRC8pvVAE7qp4ZCXLXSll78NXkERmdx8AUC3Thm3AawqpOLqyo/rZIwDAe2NV6Al1KFBTlflpA6+pAnlWdqrDEq+xZScvVxLID4WWFPo7C4hoKjp4GYcTVALlCi+xPZsTHebl6YrVmhCpXhVi2C4QtQxn3to8SdbZzu0H3NWvV03kk9x9y00inHMd5hllK6t7h7hqKjmQkt005JqWtNmXdJztm5ptJMFnn6d0CHZAxJUcHvF710FFZw9Z2WcnQgPnKWlE1hJ+SahCwQ7Y5zvNtSrgCA+8EhZhCQmWFLSwDjmTbwIA5jFqF2ZlI2wpK6Sq0E/jlIEd0EUsnn4FlgKpikkiyyPVNTzNqHZjW+cMxSTmM4r+MCc7vsKYhzZl4qvhDDmN6ywkT+fi455ypbuBB6vCcVB9GQzqRK53DN+9XJJ/8e4tuMoqOXi4vpwAwKrEvPAVHZyi7WMorQKyP5+dcyziW9RO3ZyPsyfkR7bOMR32eO2eAnqGec6ZrlvHRp5jaAkh78meHCbIuysrjuKKfOgqUVVO+fRHDvs6qdqwa0rpMCLq/C1VhSollW7iVd4zuCjDb3COpaR5rUNenaj/jTif97C/hX25M8Y2OaZnQ7Zl7zavzXYdBGVpkTpknPQ53ydKiJb5kLLkjEeYl6S5ZegSGUrOpsozPitbuKvUDvEV/58qS2g/4NkB7DQW7yrlxz3yeBHIEaJMGS51KGe+fwyzQY0g7Rw9t/8RAo8ooogieknpxXqhXKmSu2xn41ccfEnuSqMd/ubPuPOc+jyxz44t9OvazZVCdFvuNzGlTnVUaeZeZgEnRdvjqXa+jZhseDqlDgtAUrX8EifcLftl7qihkulMqhV4Ke6ON251fl7VbNq0cV00dO31EPZj2s3CjfW9C1aycU3vEIXUzhz0UwoeUmKpHaV47ebYt4eTJyh3ify8Fe16N0nO+6qrF2+qkrZ1BbSFjFz2+0QJe/ZfZf+dcQau7Jf9KvsyPuG1d9IKknKmCApKdJVUWlLDd64W1Bi6Bbal1mjCHfAMY1FZH4EHnrSlqiqs3w6wr5zmbkHRTSXZo6GKJ6sYWiBiT+bYnqQ8apIKfNlQznmMRhh2VR1KyLDWk/1elVCOajY2r5RgTJ4vqTbRYzGvcR6fYCEt5Nrw2faCHhU92ex9Fd+xkj7GK45JKre+txIAxJVu1JNnVLLiYl85yrvysEgl+Wyjau2bwQBBTMj2QknhytIeXU5/u8xznmp8gXstykpHgWDDS/KlI40Y8TIGSmrW+21VpRJqzxYVjPddYKl8+YsH/K70upKEXVF2RqGq+DgD9K8o12W5+a1Dkyn75sgNtmjNMLxFOQz6HJN7ktdlUmdpoY/4hc5Qssy5/6Uvsb3H3yFPrrReuPsuuj61qk25hsYcXjPWmYg9reGsRJ5upqmhp+Ny4xzw83zrCkudA5R0bjLaoCxvaA4nA2q415aPxLs8YMrpnu9HEQKPKKKIInpJ6YUi8IbLnb4fpwfF7WUCDxQ4YU1oX8q1Gb/sZ4gEjzP72Gvz77ZPmx0Udo667Hyq0jJcABPZvEtCZht5IsmjCnfqW0kXoYIVjMdd0p8qFHyLz9mb5lEoEvX3OvLDtOV/XOa7CyO+Z5wboK0Q2Fy4fq3DMCA6TC15in6aOkDBYTtm8iQYqDJNTh4CwVtpOL+lgKWSiknUuYubMe89PJBttjREVxVYZkJud5u85lopaePNBobX9A4Y58nTtOyi/1D+9Nl8HYsrFYDYpv9weSwN6I4qnpwrBW8yQFFVY6r5A/X0jU/MkwdKTfClrhJqeU0sNpQKNE7EfPI+21t7m/2/PstiX6j8WppLLE70susRKc5Vc7E/7qGgikXpFJHhMhAiXFIr2Ryk8ECpFXa+ye9yP6/KSxfyn97OodVj/1wlPzqTH3Wxz+c3AiK7zmKFkgqIXLXWt/UCgCu0WdjU2F7UMdqjvbfgU6aXW5Sj/VPKqbuVx/SRKgE1iSSDFZGfLeS3HZA/q5iFa6VbyKka/WKb79w95Tx9fHqGvW3+tlRwlTVUoFiC925l+7g8VZpWBcklXc6xXkXVfFQQJTGIIfuKkjydr78c1aeUkZnOdwbzLqxTIvrWF1TL8lK8UYKofLiDcIv8cZRyoH8ir60trlHFkJp21rLQqnDciwpq+wcpytymilN4lR5aypnQ2qQGdFXgOdNim3Nvs9JCXJ4vl57SCKg60fRKn8uUq6T1GroxVryvF57vsvRCF3CzR2Fvncuhvurg2OZkbV5yoZ3ayhhn5B53NMFkV8WLr1Q2TId87adcMOwtBU0kAzRdLnKLLU7wZY+TumlxoVxejFCz+d1loCAFHWIWLjng4e4GLFX/yRWl1kkdjxeUO0HVOHKjPNI2r03P15+YxYTyR+9yKArfraEvFfvK5wDXuvztIskBLraX+NZ9/vYvvMfvjhJceHETCWZUmQV1zFX8eSX3xO92pA4vueGkXcCac3GZK8gqVHDSLW1Yx+0F0ioftjkhn9rKMx2qkKtvHwEAzOWrGN7i4hloY16HGhw65Pb57u30JuZpvuNcpe5Scfn29djufJDHdKXgHkWxFc9k1lCQUlMZJTc8C2e6ZjNQpssmZcF3OOEmm2PUfB1oKdd2+l2Oc9dXibLyCu6pcjj3eW29KRNUVgfsGZXqarvIKNoz5X26ijwJRRU/VkTlrWwfV8px/roy8BXPFd1Xp1x5sSQSr1JWzRXb5C/JYE9Rm/2ecmqHFfjKdT66WRoecxzbyondzMYxW6lyjvIOdRXhascEjEp11AuUOSvkO3xV1crc5HHJcIO7/kIcG3KJHeSeby74OPpQuUu2lA+p4G6juyn3xoVMXwvyO+xRPjZ/doFsh4vmKEaZyG4KmCnKdq7cR/HVGMdyVX2sCuR7Mp1kYhz7u0UfvvKjfDjivPviDueYv5LZZtVDVzlQ8jrwzI24cVoV1Sq44Hus/iUqjvL6mOdH7UYmlIgiiiiil5ReKAI/GGkXrhJtV4c27qsqzlTh8e6QO092m+jQrmcxnykPs3JfHwQ8eKjlVCPwLs0dlYM6Giq+OlQOiqwO99yb+pcLg+EdoqIdZUE7S/PAs5yl+hn3R1AZRdQc1VFc0XyTVX5sf6L6iK0cgiPlc5GJYx3yxrw3mVZY7U4PUxbzQGARXYRV9t/WgUaiGkf9Paq/D+oKi28SXaUvFdxkK+jGVHENPnCRpqbxiupVph8oOMbq4zKven1CU/kSn38o98tVbIR4l3+fmRvETQRxMSGv/4kk1c92No23bqrG/NM3ZqW7n5gnfqjqMhWi4vdK19hss65hS2HHXaVYWQllV9IuagtmpKuE5OVYGRYvr5WFMa6Ao+04bqV0kL5Qzg8d6KVVYzO+GCPMqO5mhcJwJROZp7w9r4zmmIx5X2lbQTqSm6IyB36gMO4JOjBLhcKP+5+YF8+SW+CYNi+VqS5lYdMlcuwFnBu+gmKKM/L/9WkMVxvUUB3VbE10OV65HcrDhuqhDpMGfaUsyOlAsuOQZ9kG0Wd7ukDL41i+nWFWw+MF27ORo+wt/CRmLd7nnLBd8TNpTi0FpjQ5JpuuiycO25PurJ83JzNVSPw99iVx8D62PR3O9qkxLuoct4zGwj2fYeBTvtMlriXOBudh57tcL3olmXm7ddSyHMvalOi8X2OfennypDC4i+8K3Zfy1NIOJPb37jMfvuN6qMjEl3hE+XlfOfdbFzLjeRw7zx9j7isDYhDlA48ooogi+lzSi80HLkf9HbmjBfki+r6c5MeyVxa4EzYVSGNWFq4UrHKicOe03MyMzR2x1CaiXJXSMHKjy8mu1yvSHWd+LVvlRg2NPr87k+tc/H3unvN97uL27F0EKe7EY5fP3i8TNV0saMdMx2mnth458IKC1rQAACAASURBVOcK5c+sj6z6ZfVFnmXTYgr9FlF18iFRy8lYASp75NvVuxbupokg3lvy3aWxDip32f/hmOgxnuxhO/Y1Pk/VS4p5oqwPFGK9dZbDtQ5kXt1W6G+GyOSerUyIYRyLJMUlVBa240C5jjfIx1mPCGI/+DbiX/ljAIDs0fquld5r5HlqRRvrRjBC0GZ2ysqdfQBAv6bkViO2b3F8D+M5+e9ZCkZRJjdbVdlnHdmul3kMC0Tnb1fZ9pvo5pkyVI5XIxglFKqYIwBAZsp3u+BZzuFZAVsK7ple6+xgRVl9KpfBjZnyRPe66LSJ0md3n59h7vtRTrmvoXOY/fgWpnKXNRlqUO+rCtHryhT5Tnobe2MiyNhSNmpljawZttlIO20ECcRU9WcorbeZ0vi7fGej9CYKcx7wPUlwjpSWnMNtX6kpViv0Ljif0xWdk7yiM5Ec22krW+LZNMAXu2zrReP59R8/juxd2YgPyW/fG2Hiq+1aQ5IKqrkqqGrOkQOrqXzwBZ03/Z7qhNZUE9PlPLcTXVzu8P5BW0nzdHBaPaEMPfhSApZcC8MN3leYHvH50rbKwRRXRpXudQi9NaDsHRjZy1Xtfl6KISE3xvTy8rn9jxB4RBFFFNFLSi8UgQdVVW9PfAUAUB5/C9My6/WVbFWp6NMGeZghEt8LFyiAO6evHSwoEyUVVsqvLNQezpJYqEaip0Q2Lbn7uTb3qtXYhqtEPYU+r5kliRaCkLvxMhFiueBOHJeHy4lsivH8EQBgotPmk+I19nz+vYvnp378OErKS6HuC81Yv4OdC9rYrnaJ5hI97sZ7Of6frQwweaz81apPuFzQfl/RkHYD2oP9qxj8EtuezSgAQRpMUaHQw/sX2FCV9kDeA52CAgmEJP1KHFafiK1a57tHeWoG1WOiBO9VpR/IvI1Th89LbG+uzZOq6poW7vP57uUuVmXlP3ekhaleZVm8t7JD5ApyFzulfMQdItaYNA5X1XdSwywyA6Koy4m8k3QG4XeIQLcKNQQ9emsMQ76j1iTytAL2qejPkVC4/iNV6akpN/zMo1Y3aNHNz50n0btNnrze/3ReKHmhseMBUWxje4Z95cifpti3Pbn/jSqq4ep4cKVJPFmyLXuqthNcqqpTUfbb1TGuqvQ+yirg5kz5sQMoKdx8gH6cmmB/QnRZV/qFrmS50DcIk0rFq8OKtDTNK2kGxQFR7XbsKQav8Rxspvq461A1Thvz7B7HrXuYRXFGXjy0ZefuUb3yLAW91T2EeY5pa0B58qSdLORxsihzvbgueIi9r7znOsOorpT8Til30UujVOCciC04RkmP114fKiAxkUNCqWoDyeeRNNrmgJ+HBfI8M2rDKcut9QcUb4oQeEQRRRTRS0ovFIEnutwtr1ymUczlv4iNLnffdoI2saJNxDu95o46zZYwtribZVRnbzYnonRlP9+QX+6iUkJqzh1QOWkQjIjCjuQXnSo/QOqa3hKxOLtfL+meMXfLXr2J+FRV6T/gu4OQ7fRkA084RFq3J2U8yhOJbJr1kdXgWLl1W0qy72xjrtQBk6EqeWuUhjkhpcYfxbRFVHCupDt+UhXZ5bu8O+Fu/15igqwqIVkrFc8AeXKdIXqs+yW40oByZ+xfWOXnsKKkVLk8JjUFTI2JQN4sEUXNq6ooohS0r+xa2EnzvlPvQ/V09xPzJJ8h+lhMqT29mutiVSKiO1VSpWxfCb6EoBx7irIS7o/lIWS2VYjhnM+zhZzGyRRWLgMt0reVRlZpHtJTytRjq4s3zD4AwG1wjOwZn9tJEjG5iQQSFtF4LU3Z6XdlB5Y3QnHMd/cLDnzVB/WVIGpdekfj31AhjbHtIqPiH6Uu2zhTUY2cR8Qc9h/CGZD3P12kBteeU67Oa0TFd2W77nY20VOStk6S390JyYNFnG1+EHQQqlRULqYzGVf+zTp7GKdLiL9GHg2+yftSC86fRJFazIn8+Jv5DczHHLd4o7M2T84X7wAA/ANB1eImoEpdG4b+6l6a/Z9dUx4aX28iUSa/zi45T1KbHJutshKV9TieXjWP+7vk5fvmHT1H52kp8jrt5ZFt0MYflimf+Q41mT60tjRWmCn9bKbFd2/3uG4MlTpjrmsvzQJ2yN8W2eenY44QeEQRRRTRS0ovNhITRwCARJEor3f8Duy3iWBmT4iwkh53vrx8JoP0FS5VNT4xJZp7TQmSekJ8E/ljxmfvYaYES+U5d/WR/MFrShW76FWgfFeoHBG1TpPc5WYq8LAxjOFiwnaM5RfcOFCouiLthrLRD2IxbFlE44vrT5G4Sah4DiH92ALWmzrB77DfdYdIdLog4hnGOkjNuPtvpORtUlaqTBBJPoopGjFbReUxfUqP5fOaL/E59zv09thKLTGoEyFdVXkiXlfUn2uTj56zREaRcpkM9/1JV5Xn5V8eq5P3+6hjmqfttOLeJFr95JQS+oilyYfHfhPpR+SxdUs1MEOi4aTUk1j8PgqqML66y76Hqn1qSkK8j/l9PhPC2lJF9Qnbl2xwHIbnRGD5ThVX0qjigjl9xSGkW5SF/PsbOM5QUywklewsq353lIo2R94n5/dx54oI8KJxk15gPboT5xivbvPZ+V4MVXWtr/D85Dn5dGPvDfP/FOo6t+nHqcHmZRPPdylfXoNxAr27G4gd8Byj4FODaCuK9405+f4tv4xkTmUL2+TdpCEeuGxMc3WCyXvyNS9Q6/Zt8n4y5Jhms5TBy56FGRhZ/WCYWZsnk5n830PFAzhDDCryIJlTPsMSx7FRYv/LfRsDRa42YpTzxB2+2zrhOd2s9TMAgM3Bh+hY+wAAA0VaZ9n/sjQYf+4g1Puzcoo5khwkF6pkby5QPCYP/4FFvgWyfX85xWvmQyLw7XkZqyLHxl4On9v/F7qAI8ZGObL9twqv4uKanboJHDEDLozvhGTAXmwTt9NU60Y5qr3wleNbxWofdChsr9aKSCsoJ7nSrHMprMXOTea0CWIdHuYEtvI0xKUK6bB0/kEOuaYWcAU2JHNsQ3dK9ekqxoWueRZgVuTgz2KLtVkS+jRvZIbsE5b7SGU46RvKMp0u8fm2JmVi4WBs77MPRbYrtlLosscN5+eUF71zOQG+wiCYbFu+ihYX7uLOTY3FAuKqNJTIUqBLMlchz37GbRfDAccqU6FKmirwc0ph25ZqTgbWBLVjjtXZprs2T64SHMP8I26smc0uxuJtQm6Jviov5QtcNIrtY7SVFyanKjvDkQIn8JDt2uRikujPED6mKp+2FKhlaXGUy6ozspH3FAa9kClGchfv88DytPwQga7xlE5heq68IKp+FLr8ftzuofI1HVIdTtbmCQBcjLhI7FtcoMZ2F+mxArh8paCIU91fKVPn/vIIB3lltVxQNga2CnOnaGKIJ94EAMwcB/G0DvxU0/QGkjwFF63tlcGTgLLlb3LTa7ynPDB3FJSWqANtZR3UZocOZeQ8r0yKA87B/syBdcn5Vy4/XpsnX9pgfz9QyHox30VfQW3mHY3xghkBY1vK/78YYBnqQDvNtq+uOG6LKsc2k+ImO76qIbVkDdCJq0Cj7+VMpwwmvQBXluplLji25THXunGoXOsTG0+rfIeRi+a2Do8DBfD4FuW9GaRwrbUt13k+KIxMKBFFFFFELym9UASenylMN0vUZEozpF3uPlfKvLfTogr7NcU6PL0c49qSymjTrcsREjnVhljOE+30T3uwt4moMOZOGkjdG1pCBKMRVm3u0F6d77rT5YHge6dUBa3qAWoTPtyRW9n7SbZ9X+6InnNTT3CMK6mk6f76gTztJ3zuqqpahtMGlB8J9QJ39YXU2LjyGXurKWo6MLJVASSeowq/mvAeVwe0zS/fw0JVPez4V3ltQXX7BkTZ/mYWJVUPL3eI7pYjtkuKENKzAnZ0MJUUysh1yb+xzm6zhmgmqM0wCImw0sO2evrlT8wT65DopatKLbnlBAmH6Pqwwn5ZY6KYVJn9d60Mbj8h2j2RTC2VtCz5mJ+P5Rrp2QWUpM4WVQ/1BMp8qEPRRKqDiSfXyhOif7/Cd5VkRpu/t8JdJTE6uOZ4xNI0IwV5quK5rIKSipdITMnM7KdIuQAASWkJswsi1eFihqMd9u1fDfneA1UjSjkcm/diLu6oLmwb/K5gODfyqiA1UAX1We3ieznHc1dEh5mGTIU6RL7OhLh7ocNQw+dN9tm+rSznXnxYh1NTcIryrSey5HN5TP7GlBFxYk0RxmhC8ZaVtXny/7H35rGybfl50Ld21a5d8zyc+Zw7v/n15PbUMZ4UKwGEZRmBRECEGCVKQmIQ2MIyxIiAIYSYIYBFMIoMsogFQYlFhILjIcRDt939ut9437vDmc+peZ6rdm3++L7TuW66b3c9d9/r070+6aruqdrDWr81feu3fsNopkxNUUV9rBfw8liZh1zW89Kn3IpnUs+WR5jFFS1wxN1eMJNKbsjd2hyUTbQbwbFc8ysjqZx2uHMZ1tjGg+AAxTbVUMMYr1mN2J+SFUU8bWXgzThvxZSHc+5xvjgqcBzGI7znfrKE4gb/PzVP361ZBm5hYWFxTWGCYH1XZwsLCwuL5w/LwC0sLCyuKewEbmFhYXFNYSdwCwsLi2sKO4FbWFhYXFPYCdzCwsLimsJO4BYWFhbXFHYCt7CwsLimsBO4hYWFxTWFncAtLCwsrinsBG5hYWFxTWEncAsLC4trCjuBW1hYWFxT2AncwsLC4prCTuAWFhYW1xR2ArewsLC4prATuIWFhcU1hZ3ALSwsLK4p7ARuYWFhcU1hJ3ALCwuLawo7gVtYWFhcU9gJ3MLCwuKawk7gFhYWFtcUdgK3sLCwuKawE7iFhYXFNYWdwC0sLCyuKewEbmFhYXFNYSdwCwsLi2sKO4FbWFhYXFPYCdzCwsLimsJO4BYWFhbXFHYCt7CwsLimsBO4hYWFxTWFncAtLCwsrinsBG5hYWFxTWEncAsLC4trCjuBW1hYWFxT2AncwsLC4prCTuAWFhYW1xR2ArewsLC4prATuIWFhcU1hZ3ALSwsLK4p7ARuYWFhcU1hJ3ALCwuLawo7gVtYWFhcU9gJ3MLCwuKa4ptmAjfG/G1jzF993uV4XjDG3DPGvGGMGRhj/tLzLs/zgDHmyBjzg8+7HNcRxpifMcb8r0/5/R1jzPc+wyJdaxhjAmPM7W/0e8Lf6BdYPDP8BIDfCILgo8+7IBbffAiC4OXnXYavN4wxRwB+LAiCX33eZfmw+KZh4BbYB/DOl/vBGBN6xmW5tjDGWFJjcW36wbWdwI0xHzXGfE4qg78DIPrEb/+mMeahMaZtjPn7xpitJ37748aY940xPWPMf2+M+U1jzI89l0p8nWCM+TUA3wfgbxpjhsaYXzLG/A/GmH9gjBkB+D5jTMYY84vGmIYx5tgY89PGGEf3h4wx/6UxpmmMOTTG/EVtAa9FJ/4SfMQY86ba9+8YY6LAV+0TgTHmLxhjHgB4YIifM8bU9Zw3jTGv6FrPGPPXjTEnxpiaMebnjTGx51TXDwVjzE8aY841dt43xvyAfoqojwykMvnEE/d8UT0ldcv/LvkONA5ffy6V+ZAwxvwvAPYA/IrGzE+oH/wZY8wJgF8zxnyvMebsS+57Ug4hY8xPGWMeSQ6fNcbsfpl3fcoYc2qM+b6ve0WCILh2/wBEABwD+LcBuAB+FMACwF8F8P0AmgA+BsAD8N8C+Me6rwigD+BHQPXRX9Z9P/a86/R1kMlvXNUDwN8G0APw3eAiHQXwiwD+HoAUgAMAHwD4M7r+zwF4F8AOgByAXwUQAAg/73qtKYMjAJ8BsAUgD+A91e0r9gndFwD4f3RPDMAPAfgsgCwAA+BFAJu69r8C8Pd1bQrArwD42edd9zVkdA/AKYAt/X0A4BaAnwEwBfAnAYQA/CyA3/0S2f6g/v8zGjc/qvH37wI4BOA+7/p9iP5yVacD9YNfBJBQP/heAGdPueffA/CWZGoAvA6g8ESfuq2+dArgk9+QOjxvIX5IwX8PgAsA5onvfhucwH8BwF974vukOtsBgH8NwO888ZuRcL8ZJ/BffOK3EIAZgJee+O7PgjpzAPg1AH/2id9+ENd3Av9TT/z91wD8/NP6hP4OAHz/E79/P7jAfQcA50v6ywjArSe++04Ah8+77mvI6DaAutrYfeL7nwHwq0/8/RKAyZfI9skJ/MnJ3QFwCeCPPe/6fYj+8qUT+M0nfv9qE/j7AP6Fr/DsAMC/DxLNV79RdbiuKpQtAOeBJCUcP/Hb1f8RBMEQQAvAtn47feK3AMAf2CJ9E+H0if8X8U93LVc4BmUCfIlcvuT/1w3VJ/4/Bifrp/WJKzzZL34NwN8E8N8BqBlj/kdjTBpACUAcwGeNMV1jTBfA/63vrwWCIHgI4MfBSbhujPnfnlAnfansok9Roz0prxU4jra+wrXXCev0/V0Aj57y+48D+OUgCN76wxXpK+O6TuCXALaNMeaJ7/b0eQEe6AEAjDEJAAUA57pv54nfzJN/f5PhycWtCTLO/Se+2wNlAnyJXMCO+c2Ep/WJKzwpLwRB8N8EQfBxAC8DuAtul5sAJgBeDoIgq3+ZIAiS3+gKfD0RBMEvBUHwKVAmAYD//EM85ot9RGcpO6CcrxOCr/LdCFywAXzRGODJxfoUVD99JfyLAH7YGPPjf5hCPg3XdQL/HQBLAH/JGBM2xvwIgE/qt18C8KeNMR8xxngA/lMAnw6C4AjA/wXgVWPMD4tZ/AUAG8+++M8WQRD4AH4ZwH9ijEkZY/YB/DsArux+fxnAXzbGbBtjsgB+8jkV9RuFp/WJ/x+MMd9mjPl2Y4wLDuIpAF9M828B+DljTFnXbhtjfuiZ1OLrAEN/ge+XHKbgguR/iEd93BjzIxpHPw6q6H7361jUZ4EagJtP+f0DcBfyz6ov/DR4hnKF/wnAf2yMuaOD79eMMYUnfr8A8APgPPXnv96FB67pBB4EwRw8iPzXAXQA/EsA/q5++0cA/gMA/wfILG8B+Jf1WxNcFf8auIV+CcDvg53vmx3/FjgZPQbwT8BJ7X/Wb38LwD8E8CaANwD8A3CB/DAD+48cntYnvgLSoEw6oOqlBeCv67efBPAQwO8aY/rgge+9b0zJvyHwAPxn4G6iCqAM4Kc+xHP+HjjuOgD+VQA/EgTB4utVyGeEnwXw01KF/eiX/hgEQQ/Anwcn6nNw/Dypcv0bIPn5h6BxxC+Ah59PPuMEnMR/0nwDrN3MH1Qjf2tBW78zAP9KEAS//rzL80cFxpg/AeDngyDY/6oXW3zLwRjzMwBuB0Hwp553Wb7VcS0Z+B8GxpgfMsZktYX8KdCy4Lpt/b6uMMbEjDF/UuqobQB/BcD/+bzLZWFh8XR8y03goNnXI3AL+c8D+OEgCCbPt0jPHQbAfwRuh98A7af/w+daIgsLi6+Kb2kVioWFhcV1xrciA7ewsLD4psAzjXXxE3/u+wMAGHdaAIB+bBN5vw0AiMwTAIBp6hIA4ORolpzpvwikVwCAjQQ1Hb36GAAQzfPAd77kvZF6Fd0FD8LDfhoAUC8xREomqPEZXgFT0Gy3zVdje4e/uV2alZ+OhiiWlwCA1XtDAMDDNO/JnvDd0w2+28Elagv6L5ytWPZ/9HfffNI+/an4uZ/6wQAAKiYPADj2pvBYHAy3aBwzeyvFzzKf/5oZo+XRPHWU4qsiLcogMXUBAEeG5UxVZpicMZZVe0nZbM55b9OjGfS+++0YORRGL8F3poeUefSU9/SSIyyXLMf2Ntd9P0SLqlGFprGDKn0aVpdNxJOUTyrDz5/8ud/+mmXyF3/hnQAAou3HLPfhAbzMAABgJiz77E4dABAfFAEA1VwLuWoOANBKss3CqzkAIB9j24WDDMvdO8copWsnLNZLK7Wrw+8vwwN4SVqEuT36AMWivP80Q+Ocza6LdEhtlO4BABJtnvtGch8AAIYyl44ky4inswCA3W0+50+/jK9ZJgDwK//FbwYA8HBKGSy3ekhPOgCAwR6tYQt1tqPrsTy1aQ6RHtvn06EGAODGmK9dRdhXriybC4dDzHdYn4sRvyvP2X5BhJ9tt4Gixtusyr6xyFO+qVgfANDqhL4YmSjZ5Q4/YjieCtu89uKiCQC4mdgBbncBAJU+Tar/xL/xia9ZLv/1P24FANB+QDkMi12kDF1CMob1rXfZ/82M73l3MUbSYd+tBOwjgzqnwkiR/SzSe5HlL76BxoT9IJ9mXQZh3uvHWadEkMfZfT4bBY7j5JB9xFmxHWKLDjBk31rtsqyBDJhH7U0AQNyjXE09gLvDPpzOsnH+yg8dfFmZWAZuYWFhcU3xTBl4nosdxnGxx2kLS48rl7skYy7lyfIqGS5PmfQAwxpX+sYRV8DMx3mt6UQAAJttssVVOo3ViCto+AZXzds9ModpiEwtuioiGybbqu/Scaw14Sp3uiCDeDGWQ3/FtS0VY3luuFwAx3fJohILrqzD6KewM2AdCheptWXiVMgWGhcsd3kVhdlivTIev+t8F8sVjCm3qpfGeCxZ7LFc2QLDNddb7wEA4tox7GMXTYdsIDvnyr9accUvJ8l4vGaAcOkuAGCyoidxkCMDKIJUzOxVMK5x5zSLcsdR3KGMxr0pAODO5g3ee/cmnDHlfjGery2T2QNGxTUe6zZJnqPeY3m2Z2w77xFltJywfFtdH5cx1i8XZbk+aGqnMCYL3gm44ziLzhC+ILtPZfmciy7bbrD4fQBAPDrDrtjz6YrtsOqy/slzMq9IqIhhgu9YjcjKHhuW4c5j7hZbcTLihHuJaoiMN+h+Jyv6cnYtuZxO2edWUfaHw8QCWw84hN0Jy/S5FMuzJd/Q8QkQRNjPc8P3AQDLDHe33ilZ8OWCzC8UduE0WO7RgGNqGiGVHi34nmU/hvdT7E/7ab5zMWf/f7xQnwz6SIPjb6o2dF0+r9ZnwaJRyunN7Ta25b/pPc2l5itg3OOutJRlvzi6HKCZ4Xe5KCMD5AecLy5jLO/euIPFguNu4R/xmjbr11pxZxc5pqyG6QzCI84/oRrnkMgGx0juiDup6vwclZiv59wHALSTZNv+GcdGw19g4xbHwvI9tntEQSzbObbn6pI7hfjLcTgtlvn0Xd6Pr+AqZhm4hYWFxTXFM2XgwQ6ZX9Aga6oEE8SHXHZjUvFkp7wGjnRSiwniObKC1ISMyPPJCmagnjFW4mo1uligUiLLMcdckbFPhvVChCv/IudhEIhNDPncXYcMwmS5anaPplg5XC0XJa6ysaV0jYbXeGEyjER8hlCU75rE12NUAODWpQ8LkQUF21Ocd7ji70S44mcmZF7ZgDLqzwcogtcv3yezie9Sd7mQPi3l8Lmn4xE24pTJ5SyjsvPalViau5qjNj0CAORXZPn9Jdl0d1dswW9gFpWOzlCmwxrflclS1xiulQEAZ6kqoobvSLnrWzktl2StTvwAALCbMpj3ycAHO6yLE9IuLkTWspgfYzvKCtVO2L77MbZLJPEbAIDjS8auCh6forBTAQD4U7ZnFW8DABJV9qWJ10Lb5W9Jw+fWR+wnvUsy8oS3Qi/Dd4VHLJ8XZt8+uiDLnX/8syzf5RjF2Gv8f/9N1fR71pLL+x0yy7DPehV7mxhvqC+nxO4fUB69OyyHN/LQnLGf51cHLJN2l4MU29PrsQ8lFmk8JEnFts6Ami6vcRdkguFsFVlNG8GccujqbCG/4mcxEsPDEdvdzT8AAJiO+p52lckV28K/qKGt8uEd7orwz30xDPlXReecbbJYUCaV8hjFKndTnTnH8IVPij/rsg5JhICJWLTabxrSGUiffS+9zb6dnB1gnn4IAGhr5558yEOqSZbvSbcN5nvcxR8dsy6NIXd7N+9ot+PG0ejw/Zu3uPNxQuqnFxw38QOOmcHkAqhyrIcWT9/VP9sJfMlBU8pycll4cywzHIjpOQU3jnIrEh5we3GaTyCriWxR5vamcs4J/EKHcb7Lg6N01MDPcbDFE5xU/Cknw26O73SmU+xoojUcczjrUQxbUju8V8kgWedgqerA84bLhnkv0Fb+I/x+fDhGS0IulEdry+Qix46cujqIHR8ifoflc5VIpz6j3GoPWd+h/x68DssTjvGdrg4qvRRVT5E+B0w96yKZYQcuTzjIl2GWd9xiB2+aJcIj/tYasxNtpdhp++d8ZyLuom60mM35znz4XQDAxpip/y5uSb0xWyG/5AQQ95ZryySuE7BhUhOVn8TDAlUU2RD7yWCuQ9YRy7cYLtGc63CxzIm33WObnzcpk06Ng+rFZRrLBtvXK/Oauws+7wtFfp9YFVCLcGub7PETA5YrFuEEOHQHSA04Ibl5TvztOSeuaJGD1DtSW2Y3EfIUlC4WWVsmADDmMIDHYYBGbIrtjlQSwSEAYFrhuAlxPYLj9OFGqEpIar/9bpu5FxIjHs5O47znMr6CF9aBr8uXmBHrHptL7bhKIJzkROT6HHchcEw0zQmvnWWRM4oBdaGJHOyf1WlaMuAkGApHMUhwAgvff7y2TEI68R9LdZS/yKKmPrLw2KeTLtUl+RW94Feuh0yL9QqNRL4iPHzci1NWgc86pjOHiBxzLhmp/Sdjjp9VnHUaRC7RbrPuYRGKjQOO54Vhn6v0N9F/keUyl5zURzpcDbpsu36O8lx1HYxEFvKRq/hgn/qy9bcqFAsLC4trimfKwL1bXGke39f22kRQjJIhVx9wtQxtkWm5Yx4YZZ1duFtUIXgprljJJFnrzSrZkp8lk8zmy8hFyQ5nfTKIoUyowi2u8r3pGLEyV1evw3dtxvl3p85VObo3RdwjI8vLdPFiRGby2ogstFeTaWMxQFSHq4NgfWb1QpqyCNX4/MluDHtTHi72QUayccJyRhyuxn58jneLZAPhC7LNWoxynE7Jilcz1ntj5mJ6ye1YOEYZnwVkAiWwvvPVPrwwr0mFZZp5W2aFH8h8ypAi0AAAIABJREFUz80g0uT/F7fYbeYRMtvLOutv6jIdjKcw0a5knF4/HlZbh9KzO2SI3lEaQx1ehi747oLMHptpMpxYz8VDh9vicJ99526PzMvd4TYe2g34ZzWExJQuztlmRcVhejnD3YTnNXBWpbwml6zDQYbXPhqTtSfSM3QnZFrxOvtQ3KdM3SzfGRlJJThPIFzQgfX8aG2ZAMDtsdrPZZ+pDGYI32LZmlO2tzMhW1yl2U8bIx+9Hg+dZy7HUSvC8uscHNEp63BiZshKxXEWlWrC8LnFAvtkrPEmejqMDXco79CKbRIr8J2teBZOi6qjaOQAANCuUnbONuXkhyjveXsK49H81Gzk15aJE3AcmDf53G7yIXJTtu2jDMdGPOBuMuZwDojenSJ+n+/vD3ntfM4y7IDXDNM0BgjaLqL5j/A5Uj1OUpTReYZjxulVkOpTBoMEtz6TMftazGX7pLsphNuUc7JJuU08vivkU26rJuexRugce2E2zpuz1tPr/9RfLSwsLCz+yOKZMvDPPqI+sKTzxcnZEJO7YodymnDEhDIx6n2d8QixS7KAWIWrbHvJdcfRIdWVTtaJHiInRtQfkPm8s+RKtjfigaffXuIDl2zATZLpjRzeHxvLdLGxwjzLcuQWVDxup8j4+iEy++CIK/dkz2Ce5X3+6MmEJl8b0kse2AxucqVNTZb4wNOq3eAKnSyQMZ2FWP/Z8mW4rd/jO7f57tyUdYhmybYyCZbFK2xh6lNenTYZ/FZABjV/yOa/vdHBLCy9pnYzUyVi6SZ1iJmIIqxnwhPTrvO3kc4tQinKuld7AG/2EgCgmFr/XABFsr+7R3KGKJ6h8ybbtRfIieuCTHcKMp20meOVIcuV3qMu0ZGJ5UaYfSkWZhtWg1Ok4x9nfQd8niuflulKfSlSwlaTbdIJs5/4Zb6z1JTzTstDZcwdQDdGWVRkcjZKkLGGIzpo2ZogpZxAzfGHi17cz7LvRbs8YPbMDN0PyFrz29Tv1qes63KDu9PJYoXIB6zjTE4mY52fRFNsa1niIV2OwilybJhDXrt8hWV9eMp37yc8LBeUazLDPjMYsn+1tRNbRtq4my6qrmzL2R2O4VyP7HVkdC7hP8BGjWO/Z9YPSTS/IJtd5OXAN+rhIWhSWxnyt3REB6ZjagBSv2swKbK9Zkn284MzymhZo0wmm9z1np1lkb1Hs9Y7GT53uWBniTa4o+lUx0inZKJ7SN1/NCS/G5/PqU3nqIzZtzp9HTjfpUxrM47zfY+/byccjJcsR+Uk89T6WwZuYWFhcU3xTBn4d+skelEhS7zcbsN7KMeBW9TdlYb8LevInK3fxDzFVXzDJ9vakjPAIs+V9WJCpha9TMJPcnVbbZItbre5knWmvKeYnSAUIlNr7nB1y57zmpOXeJLs1pY4r5HFhQv8LdHjKmkOyLhWGVmhoIvApf7Mya6vwxvOuOp2h2R70WUSWyHq7IxMty5E2JwET/ALB3N0F9TvmRHlN5creFQWAosCr72oPkZjwXX6u3LclfxWn8zkNbnqX2TaKHdkVueyjfya3IQHZJ2emwD2qG8s9yi3WouMPn6T97pVsuRC9mV0omJgi8raMtmVPj9WkMv4BykU7kmX3uJvE5l5bl2ybtNCAaE6v1s50kcfUrarvswBD9iGm5kIDs8onxdjrNNSO5BhivV1+hEYn20cW/IsItfgrmJT5pjdcBOhqKx0pizfUDrP1Jj9OSfTy/r7cczKOo+pnKwtEwBoPpKT2gb16+7gJhYq91lLulztLsc6j4ikc4jukvobn/JIKQRCfMLhbxT1PQidIjFkHw62KLPlEftVQ84m7WXii7szX+EvonGZ18XYh/3kDmraFSMth7grdi7rpFiLVjohE6AhN/t4av18EP0BWfCWYZ3mlZtIdumE48vUNHYmnf8WZTIOzTGSuWCc1USvoHAVhvKcnHHOSQ6A/jmd3H5rm+Nl45C7eSx5bZB1cZbjLiIly5RYmG2VcfhcL3OKHA4AAN08d2WxS34mK7z2dCWT4IdxeA3t5PLTp9bfMnALCwuLa4pna4VSIgNsHSt4TfoA7QxX4tsjrj4RMd7+Me07e+EU7h3x/rgYZDsgu3nNiHHJ+MMxA7Sn1K2lVlzB7vZ5TQt8/sR1EVLAouySK2BEgZsWOvl9O5vHwYxMqq4VeVnkWudV+fxkhqtxz+TQn4h5hDpry2RZYvnOPy9b9jvvItclU4grCFPqUnrpEMu0+kwLZoOMY2PJd9dlPTALcwfizWXPO+nipgJeXYByv5fk34sh6/9SuIQHMcq0ECUbSvbITPsp6nLjg1Pcc6g3rC0pcG+fjHYxoCwKJTm6OCmkpRf3S09nEF8OfprtPFfwoV4+ilyd/x/4srsOkSleDtlPktklPFc+AAppsLnL+p24cueXjvwW7iCaYFuFZmzzScBdycYD7gCd1CFO4wpp0GJ/mc3IQjN7ZFml3tuYdLnDmHuUXyLL5w2P5W6fp2zmeYNRQJ1npvkHsm59zTAKntSYaCd72kO8RHlcJMg2Xwuz358rOFquP8TYY1s2puzDiSTr4bPIKOjsoutnsNI5S2bFH1sKbvbdAz7/UbeO6TanjYzHPptTMKvWVMGjonW8OKXMuz2WtRvnPSXJq17itdlHHoYk8kjWh2vLJDTm/DHb4o4v3vkAfuEFAEC7Lp36bco9vODZwWzVR3rO/4eq3A0t+txFpFOsy5HH3X3cWWASomVXYsR3PCpwvjiYUTbvn/p4zdPZ3YyfeyUy8pEc2pKFDHqXLMeywGdPO3xn0KH883fZ94JxHe6KY7SwCD21/s90An/coLDdHieZSPp99BSn44ZMm4YTCtbR9n7jwRDDFDvue4pO96LHLeyn1T5zRZS7XZljoQ4zXCr3qDptt6Jti/ERmmoLLMP8RZidbT6SM8B4Dm/Ja8Y9TpAVsGFqKTZM0OAgTO40MGux0Uf76x9OtdqURVneXrNIDKuVJq4hnxtoWzyY86CqE44AZ3x/9q5Mk5a8p9GhHJNp1j8Z7aOliHKRA8rAkV9K9tso1/unHiJaqMaa7EtXh5r3qTJ6VIyidCznGZmYxUoyy2pzEhl4nOjmfRfQ4dDNDxEfJjrhpLzy+AynfoqFUWyOAdslKWeufF7OIx/4GEa0+O9wizvpc9LZiHFgLOr0gDxy9+Hd56CZuZTBRkEOJyISrXAJRbAcxQTbaBilKiLV5EL21qNNrGSyttQAThRohhjZ5KAfz9lJnekGwhvy3p3W1pYJACxn3MrvtvnMy50AswzVMndHIgINTow7t+Wg0skhNGDdFmHKrh/mOIzlOcmnpbqYVw8Qvcn+cz9gm74mM9/hAx1qvhbD6vNsd/cu+8iqyXZKrPieg1kcE4fXzyssl3fJ8gw3pbJSRNFp0sEsocPQfnRtmbhSHY4bHHuxwhY2a3LSUmTOblvxlQq+yjJAW1EIL+McJ+Mi1YEpOSztST0SSr+EzR3292FNURg3dQB6TDXR7VwaeUV6bEZIPrbCHLPTkcwbOyuk8iRD/oLxUhw5Gi1EzLbfZrseT4GYz3e2Jk+foq0KxcLCwuKa4pky8L0eV7CuogB2JwblGt1KPzggk0hd8gCiJceEe2kHjSMWM7/L1e14wAPK8AOu+Okk1RqP80MsA6785QQPKUYyoYvel9rkwMGZTNuCCzKaaJOU9CT1eQDAxeUN+BUd3o1533RDBwxTqQ+qZLOXoST8NL+LPVhfXVAOuJonQ2QEj1snGMhJp6QDyUBu/N0TsuNWYo5ljuXJLsimcoqp4siVOuLI2eNGGpEqD728GZmon9AB6IIMdW+zjXCF73woLVBMh5jFV8hw67F91LXryPu8L9zgjmAZe4Wfutkt9+AsFBclcr62TOYLbjVjM5azF++iJdPTTJzPG2yxL1RG7CfL0CXCecopLlYcnrNOntRnaUfb4uM6HnZYv2CXTOelpdy5yzwUi5sSkhP+NpbZpBeoL+j5L397Cw9+nUx7O3cV1oF9/Mi/AwAwYcrPq3Th6EB9EN9YWyYA4FQpl1M5R1UMMBiTVY9KrI+psD89VAz9VCGG5APu5G5P2T5HE9bVUTyduUzebhdDMGLp35OS2iXgtZMb3P1GOmmU9hXGQbvH6Dafmx+SYV4mmrg54Djs1HnA38tynE8dqeoqVEeEzE14De5Ilpkrt/GvHf6c70mp37crMXh9tttEDDx6dbgdVWyUTcCMuZua98nOw4rZ4075vKvdeTZbRUsmpo7iKZVmclyKcbdlwr+K9pT/Lxru2HtSk5kkVUY7owb8MOeXaVou9U0+LxFSOACfct3yymhkFQGz13hq/S0Dt7CwsLimeKYMvKkgRakVV73HwyXyr8j1/YSsuuOTUWXSZLjj1A5CebKi4OrwRXrumYIdjfp8xsn7c3y8T3Yy2CRzCGpcjT8IkyFNI0OM6zJt2+Bvfktu9x6ZUT8cQ2VGVtEfy+nhWCZ3N6lfS79At+jQ9gybl2S2vbvrH2Ku8mTQJ64cU/op9BRvOiXmV52SHRQqdE+eTAx8uSafjnnfbEpZ3NDBnbfF+qacBfw8D5tacwU1komTO5Ajh1vCKxMdKGRIN8avU1/buCQjq6yGmPs86BuCJniVCSNJPk6SZZZf4fcxbCHSYx3C0YO1ZTI3LGdkQsabiScQUSakUZKyTsXYDjMduu6YXSw7dNRInlOmcYf1jk6UmUcHvY9Lcbx+xjbvrcRGdVjk91heMxrDfYGM0tTIgiJLlmuu45XVYwdhBR57V6aLW50jAEAQY9vNxvw904simWV/qy1O15YJADgbioGuMAC1yhLhI9Yt57MetTjff1eZi/BiA9MKx9tI5o8vKtDb0YDjIC6Xene/hpgc3xbDK9tVjqeEzBTTlRSg+PyOXOobCsOQDbEPZS8MDj22l5PgTsRTALvYIcvQdimLaXCOWY3t4uRXa8sktiRrj3YUYfGkidUnZFI4ZRvcWLGcvTGZrhdbYXbB9kpldG6iHVkrTWbeOeNOL1TfRF3OfLsB2XWrx7r5XY61pZ9GusL6NSaUjW841xUyDGBWrL2OvSaZ96ovl/wF3/lY08amTpVTcRdhmaHGqk+PcGoZuIWFhcU1xTNl4OMbOtFucFVZJocIfpurYlQ597Iylo8meE05CDAuckUdtsi0L4dcNTNpxgfPR/mMoy7Qvit9pUPWfuyTVTtzmVfVgZaM4wufV8xjWUx4XTKcbTNEfSWHjNv87E5kDha6BwAIFOwqaO2i5crBIuGtLZN5g7rc6pHiN5fn8BRGtiPD/lRSZwYyFXPbKwz61GuntFNp5ln2hCN95ntkmO0XwpjptDsx5zWVHTG4GXXC2VYXX3DJbA+bvO+1Md99EmW9d88mGDtkbBGRswe3yLjMhDxgqfOGVTiJwVUu0tj68cCvsg2lFUs62U7hcpflKfav8lySnXdkNtdvRDBXrse5q3C3aTLdaI46+tFAuylnglGJ/SvQjs3IimQZV3x2L4pJTZYJSZnkKTvTbz+Qi/ayBl+hebdlMTOVFUJoQ5mMRK+6nTqWd/kcdzxeWyYAUJOzkKM4yNH2NoreVdxu/va6xsTFhLuhjbeOME2rbNqx+jX2g3t3yYJPz2XJNPJhmuzDfWUa8mWNkcopYFl6gqqy9mQUZqEoa5+OmOVqN479BRnkcMz76w2dKZXYbuEuy91p9xEpsxxKfLQWHJ/9ddpUcLlSGVHtELanynSTlJNVS7HTBz5Op8oxoABf6Xc5T8xfJPPOKzRCvJTC7raCoF3l1lzxM6JdRCwVw1R69mWclShI/+59TgXdPcGFwnGka5LBiNYomwoyt+GwDu23L5Eusy83VodPr/9Tf7WwsLCw+COLZ8rAl4+5qmQUonXYyCC4JeeSCXVjR3KEyIZ4aj1YDeHMlHtROrvKppxXBgoYo1COGexi4vHZnegVWyQTmPpkHbVaB+E43zkKk61MwwqIJNY/XpwgvyCDiRuFuVXmllCKutd+QxnLYy0MxMa3G+sz8KtAScUQ6912ithT4PnhiIzmskdGMW6x3pH0ArsKlenLlX4jqWA+YkyX22TU3nKM3R3ZuztyZx5Qt5v2yHAbrQqKUeqNm1EF3xmSQRZlsdAq9iBPaTwo8fR8o00mmozx3GKxVFKI9hCLJlnizu3E2jLZXpANRWXt4mdDSCiY0kgn9eUu+8RrMeonL+/0kWgopKfyqSajHwMAOIZMLFXk96HhGJMC2ejZTPpRZdLZ8Fi3VShAUJFD0SWZ05lcvocB22U0nSBzwPvfG/HdKSUqcPW3J7v6VdlB/SGfk5Hz2rpYbJLtO23xrnkbywi/yyzZ/o9bZJtFOTwFW0BefhcpRZG7yv50/jvsK8UXFOTr8gbKc46XAzmKVX3+VpCLfdVPYsPw/ha4u5ieybJkR1lkpiVUfbn2p8lwoz2Ox86Qu5qRfjfJOnoKm+Et1rcDr13wOfld9sX4xIcXoUVI44x9Y5Zn37mKIZZPu7jd41jwlYvX2Zd2oMdzk1mJ80WjNsc0xboX+yx7Rnl2Z0lekxq1kFSe2ExSWTcMn18y7A+HXgSZOuUTjbFtogq6lZux7GbG57ovxJBuyW+lb8PJWlhYWHxT4pkycMjWOK2YNWepFfozrmL341zlMrK9dRyuQI1BF10lGbh1gyxpeHrA5wzJIEZzrmzp5AwbsiqoiqnV82Kdj2TFEG8gM2NuwnaK10RkanlYkW4q5eNAq+5U1iuPXLEno5yTGSVDcJZw5YG46q9/ih6VC/tjBdXJ9kfoLuQxJ4/HXJwsYajwmM6o/cVcjrMfoDAHSj6wFaWMEynuLmI3zlGNyBuw9iIAIF0mFek2rpJo9BH4rO/OOVl5PyATGYxp5zzJ59Eyyn05YptFIrJC6VP+dzzuYPydIiKb/O69o/WTXPTKcksekH3MUwUEvnSMXe22XiWbHgVkYK03FshW+M5XN8ii8we8diYPOmjH5iYA5alAkKJs+/LGTUWkR8cCzXPZ6IfZFyd1nTOUyE5T/SyaOhu5VyTT7M4p96xStPXC7CeReAWJLJl3RrbV62JfadJaL5J1J1Mu+qfySyiyf5fUBcMT/l3uZtGM8cv5qfT8EzLnl/OsX0t20/HwCQYZJeKIKtDVIyUdSCi9Xc3FOEeW6dd4TcjhrnQhmQ4LbUDev+Ee+1XIZd0D6YZnl9JPhzex8inD8PpOu5j69BtZVBlorFrKINjgmHU2OWZ3FrLUKtPLO+jWkcwoLITmn67OzMpi61fWOO1XbsBvsE/4Ifa5nRd5z2zB/tk+u4VcTn4nUe2MTzjmpi41CdnLBlIBn1lUztv3SvQYzr/FMRvNc4dXmi3w6Q53UlW1zVfCM53AZ3M24ipEE7xMa4jQLQ6u0IifNcXyGEY4MMv9BnyHwjhdUpBbBW4cRiGqGlqjAwBA0lng0KFqY+DymtjxEQDgN7PsZK9sfBf65+wwS9X+MMttTnqqvIuLPN5OUMhbMiMsvcBOO5Eb+lgHGrNyGOOF4g3Hn250/+UwVBafTEwHte1jxJVlJJRRsub27wAAOsqkkxmW0U5wtEzf42S/u6V4H3nWv5+jA0Uoew9+k/8vf1SLWpv3hFTfdqyI2fucpMKvMo5E9dc56KsJTobzUQxOmu+IaCKMxNjJZoozfpLmNnKzV0dG8WV6WH8C/+45O/KDG4rn3XVwmmbZ/RxN+aozmYoF/HtWiiFwuLhOFXrh8F3GcY7HOIksYux/w2EaYTmvXDS5QM0VhqN5j/0v5uXgKHOTO2Q7LMI8ULrQ9j/7yRgiykJTG3Kgnaw4Od5QJMmcEgcfezN8V4IL3mq0fj8BgMa3sW2THfbffpDEZoUTTl0H6YWmssQoKt7F/AyTq8PGPCfVlUwMD5X4el/qhLNJgBfUj/onfEdG7vHxJOtxWg0hGzoCAFRctrHO9LCKsz+4TgVLJewtp/icd84UubAilcwp5XWZnSIIOMm1wuvnT21u6rBwyH52cxZC7pKme70c55nTM5oyZmVO2A1WyGbYbwpaYHI6NMz02P+jcfa37XEdfYeGCy2FsFkNKTfPZ3/PpY4R7bEDZWXOONUCOB5y3pn3WvArHEsnCs+RCzintDb4rk05OV00y8j2OLlfLI6eWn+rQrGwsLC4pnimDLwkV/DmnoJa7Syw0lYrpEhmCmyGWIdbkUlwB2VFcRtfkHGfbtOtd7ZFdrDdVjQ17xSdM7KL8h1ul4YRbWkTZErH7S5yCa703ZniV3s67BgrAM+NBfaiZDutJrdWlQfafuugZhzjvYlGCttZfjcw65vMxZPKwXfJ/WPwQgnnXdZv50gu/gesS1+xr89NCxNFVAwpP+NemAc34Sxl5PQUYCvu44ZzAACIKQNRVNEg61tkLZOZi9DH2Q6tE2UN2aZMcjL3avfDMAHrHJPDRndIZjc9J5uJXbAN214GfoJsLL+9vnPT4hZ3P3m54yeXZ4jKfXwVYwajpAKSTdvKBerFkEmRYS3FgmrKYRgb0DRsuFJGqOg7ePyY9ayGqBqY69A6d8H6rhYDlJXzcKXd2INzssdYWYfe7jZGK7ZRXHkSN2Sy2e2xPa/SPN5ED23lsryzebC2TABgpV1CXHHhnQsXMWWlClweJONMcdNf0NCezrA1lKpMqshxnzuyXQVXUmJ2bPlTvPs+r80pUuegQrVB60RmgcgjdarxFlewtTLlXVjymvhwiOFVtqsGd5PxCMdR+1B9OuCutTRfwsRU1nhpbZm8cMT3eBm2zTg7gT/m4XWQZBuUwuyfUQV4aw5jmLz/KgAgVuZc5BVkEpuWaeCI4+uwn0ely2cXP8X+P61RxsmhAtyZFbDgfDHPcztSbytwVoLjcjVaoiSHvFFS0TuVrSkmh6jZ56Siu3mO3ox9LKqD2K8Ey8AtLCwsrimeKQN3i6Qj2Q5XmvPiAnmxkoFYQgpkRr4Cz9xMlzFxyG66c+q04htcWdPvKH9dnmxsv+yhPaZeCleutdLBJar8fpr10JOTys2p8hnek26xRb1l9GSC6I7CsSrudGdTh4Zyu99wWYau28fjDsu+l91eXyaXCsCksJPt0wC5MJnIRYar+ViHtR8BGc/p3MOuwk2a11j3upbi1UDpVXL8/qC3QO9VPmewFFvUYe0nJKveno/DX1VWobriWO/qUEfBwSKI4IHYaVzOUf0Zmc3qJplI91hMZxTFaZltU+y9trZMxiUyxZiCZVV3p0gocJlzTPkPSnK0EqvayMzR9cmwqzpgHsmsMN+UDjtJxvNP2sBcMcLPHrIvbJXIst67L/1+roZLj4xrc8prxsrktMqxjy5LDxEeKb9ok+VLbOjQMk89p0lRLx8JRTGe89ppav1dCQAEioef6P0WnzPZRCglRxs2F2J3Wa9YV45n8w7GLX5XPech+N7LykF5IiemLJ+Rj8UQjFj+wbbKrUNjpYHEpDlE/wafl5bTV2iug0/FhV8VM+h3OLayS4WD0GHhMEe6f0cHevdTG9hXtp63ZAK5Dmphtvm3JbTVWR4gJX32VE5Oowx/iyrkbqEPOAqyNpzx/GvR4i7X1YFvqqQxfe4i9FGOF08HkxmwTuc+9dvjSQSVCMdAp6dgaB7HcDzO75vLImZyMHN6lO0HfZm3ZjiW0x/jIO6tVogq61WQfXqWIsvALSwsLK4pnikDH5W4Ovl66+zIoCN9YKlEVtJyqTNzd6n7qT+eIRbIpEmWA95EWcLTZEgbC37mAAy0Ei8qXJsmypgd9cWmwlHIQg7VJle57xC7OC5wVQ5yt5CRNcx0Tp36ZKHclTEFuZJL8P6kjlmLZZ731g8nO1gpZ+Ihg+jE/QkGn6BVR3r57QCApKwyqluyFHGAsypZQPEdXntD+t/ggGyomCA7aGZjuKmcmH7jOwAA2Qjt0bqbZDy9zj72P6IMIscMTDX7LE/B37lU8P/wx+HMj1hmR6ZlDmWSmNJVPbxNFrsx9IHEAQCgHVr/XCAXYj3zSQXUGgYYKUhXHgptoPC+3oCfj1s3MSuz7Mv7yqqjndvZlhyOrsKK9hv4vRZlOlFyg5nyRK4i0o2PAkQ7bPv8FlnesES2ZsC+me30Eej6eYl905yzfMFL0r9uShc8C2Erwv7hzT4kb6rxXQ91XlIcDHAmE9PCSGa3cgDxrxKYpDcR0rjZUGjk3JB1npbJipttjst6/ABbCpcwn1F2KeWovdqJ3ndHCM25owvJiWo0YJ2PpO/+nuIU/pSWGx9EZbZ1Q23wBd4zzPKdLy5TmH4n6/Wdp+vnxEz2KcsD5YDtrubon3L3E9pgGUIa+36e12SCHhKK4DXr8YwsXOX4qxTZt72A5Zsl00gpSUYgS5wIqAnYVPiCxmEI3j32lU2FTthWAL52i21124zQlPlgTuGNnRzH8EaEO9qLI2WJyi2Qj7OvpHNPz95kGbiFhYXFNcUzZeDOQ4WfzPJzdOAhNeHq1k+QJWwPxQB6yvEYHWIu/d4oRcawipE533pNThxnvHY+vI3CpoxSK1zt0vdZxciGWHzBQ1iBrboeV+Rq/wu8JqC9aHqnhnpXjExlDcfJ3l4JHfCdVYWGdHcxU8KFVmR9cQ4aZAKrG3K2iPcw7VPX7UUVRjcvC50ly1DvlxHdJsMajRUGtcSVPlom+xgMyJxu3Z5i1aVsR0tG1unNqIO7oVx/MT+GMch2Z00y00WbOsGLC9azUp6jHOPJ/WQkKxHZV49zSuRQl9t9ewF/pKBYN/fWlkkoUHjg+3KWymcQ1dmI9zG2R0mhcF3terx0C6lH/P+jLNneSx7tro+OyTzfWP4+AOA0ukKhrPOEOyz7+1f6fVl1JMN5DCf87qxIHWUp+GMAgNx30c7YPN7DZCZ7YaW0O44r1VuY7CyqDO6j0hLmjHWoVT6cDjyvlGDjJNuvN5hhW0wtqdyq+yvWdRHIQSkxRUE+Fm5BDjhlndXu4rueAAAecElEQVS8w11aRNYfYcywCLPfbCk4lEmLAWrHc7MXQrenoGUbLM/0TY6/F5UG8SQMRBTm1fcpw1uPFQr6FsvQHtKp7CTSQfIdvjOkHc862JGZT8fhOJjuRzD7gO90myxDRKEPxlmFuK0V4MRZh4gSV0x0vjaXRdtqwT6+8MYId2in3jxSmOLbtBJbdSiblN9B5Iz1uy8Lre0TzlFpOXhdfMHALyjRhJJuODWO4dlcKQkzLF9ssETsFuefvcdPzxP6TCfwKiiAgpwbNrs+Yi4H/bTPCicUj3dWp2fVZq6IqstOWolxItqcUAAJeXXlUopAGM6ilpfZ0gMKpb2t/HieBnw4QOp9dsYbNznZRTvsTCOP7+l0fRzILazjKQ6yPP66MwrZFDlAlqEBeg8UN6S4fk5MN6/4ES2Zvl1kkCpr0VG0s/iE9etesHO8sN1FXQ5AK6kq6icyuZtzcsiWWLf+/5tF4Q4H/M4lO9O7mmR6sh8bDSJoa8D1h4rcJjVGRQfG28UMOlcylCmf6/Md6YYiPibpOLOaHmC+ZFlD4fXjfiwGnIT6W4qjcd5DIiGHmENOAPtxvrua58DLzpoYTxSfRj5NtVd4fzJF1cdUcUMO0MRMMV8+PqbjxoMwPfriCT4/FXWBjvrSltRwclwJhxQnp5LD+VhZe+SV+Mor6i9tfvY6fGeh1Me4yDocxHNrywQAghwnkvQXPsPyVPaR1fshL0a/p6w/ih1+q73EpdQhc+UPjbzJhW2svJc9mVe+VGugvc12e5BiGV/Psx90A05obqGLWVaHwyeq+44O9GWuGX43gkiG8g3pMPVCXnOzc8W2l/nlbnKFdxdygtpYXy5jJY2+lFFB8s0elnVNnjf5jjMdOrYu2Z7JeRunCy5a+wH7eznMep9MpKbUvFm6/QK6S47vwkfZpv232PceKSmxO25idE8e2lJhDRzlsa1xTpmYAFWN47tN9rXlgmV2lWQcc/a9mNfC5YQmnlXn6Kn1tyoUCwsLi2uKZxuNUAr9yYvcKsWQhKMtX5Anm66vyIhcKEv6OIVFiCtgSO7BZsAtUvIu70lHZH5zXMX8jFv+Tp/bsv1NvqsR4zMqpykEMu35qGImfxYy/VFW+u7SQytDs7r2Md+V2aI5XFKs/2SqQyu/hawym1++kV9bJuEo3+nI4ajiPUJK0cqmynfYH5LNtBTvutes4aUNrviX1SNeu2K+vXmWz5s91g7h9U3M36Ysz8Sqb6bI1vu9j7Jus0M8esTr3asY5DKh6yoGNoJdRKZ8x4YOL70c2Uu9qq2fkgc2Fw8Qlonio0l7bZl4XZmFTuUmvh2B0XN2FMIgiJPNvFRmnT5z7CNcoNySFdavqMO2BzOyreQ+D2bDy3t4fZessyONW6ZJFlidULVysJfEtMl+MgkrtEGEDNZXLJNeZYzE5ynbjE+5zea8pp99HQCQkGt98PgxjPKfTj5kTsxmj31yq8S+YtwOWlLh5BWx8igrtqgcqfVKEv4JmejNERne/UCn9qLtuXZX904Rl+pEqTHx5inbNqOd6HARgRFjbmrX19OueTHkTieXLuHBinK8t2IbppRpKzSh+qazxTK0JwYHOcql6ZysLZNVcMDntvmewsaLGG7S/PShrBKXJ9wZLEvaRaw2EVVsl0FTDkoFMua5TCO3udnBw1YKZYfqROcL3D00ZQ4duHzu9GEBp+9IVXSTY3WmnVdkSTlEnQu8uuCYnSzlfDVXvJMWZR5RruBpfB+3T9iXG/OnRzi1DNzCwsLimuKZMvDiPlfdwYwswV1twVOkMF/OOJDuCCn+PZs9Ri6kFUr2hycJOU00qd8O4VMAgBFGWE3IHE9ailF8la+veMB3Fi9gZIJ3pkON6Cn1hoHMgvKzIXy5HUc97ghaZ2QHubL07YYMJ4EZHtZY1mX08doyuRiS/c97OlBcBmhX9yUCMi4vwlU4fYerfHZSQNUhU6iluMLn5BxVV7iCaMC6taN1NKUDLYTvAgA6PbLZaYHMsna6gZLiQL/TIbvLyIHji5HiXj2F94CyiO9Q7vUOWXtC0e1WA7ZvfPtlRJWhZuWuzzY7/Tf43IKCRg1cXHSvnLbIUkIeD1QzM9Y/bppwlQc0PqZDUWiPbO8qINOsTjluvzIFytSdl5qU09EGr31xrsiNDQ9G5pK5gvI5hlmeOMi8kL+NUIn9bTBRUK0ZTde2IkcAgEvpWjf6aXgZmRS+qTQtf3w9uUy1+7uUkv+11By9BjlYG6xjSgf6RwO28Z1FgJjMbT/TldNYnIw5myXzjdTIKIdJYPJIOvw47++P2Y69xzxfGCebMIp57bh8Tllmvb0I3zNxA6yq7DdNI1d8HRbe1KFoasm/w4kJek0eOr+13FpPIAAiK7afF5UZ5eUpFjpcncvpblGkPnky5Vgb1GKIpbXLTZEpX7zN3dk8y/no4oL37JR9XE7Z332FK28pNn0nrnyViRkiNdZ3kbgKV8DyXHX/pgFqjYcA8MW4+qs0Zb284BdvN/j5z7w8xDjGOSC39XSZWAZuYWFhcU3xTBn4b8mx4rvfJvvpfayJhcuVJtulfrUzofVJTKEXE40cliOyge4jfbcnXXCUDCkelQPHCBgOxdqumO02menOUiZPJoFGlitdashV0tzkPZMqLSZS6QxWU7KSSYZ6vawsN+IKAflRudHeb4YwA5nIsDNfWyYb52QQzaTyU06WmGzwOfEZ2XAQ4oqfPlc859AOpnHWeXvCcq6kL74hi4KJckcuTmdfzBg0d97jtTr0NqdkCx+8P8duiDLpRajfzAzFoCMK0lNPI67gQDNZYewY6n/f1in9/jZNQM+HM+QWlH9pc33TsKwcT5wFGWM1WCFSZN9ZtGjqueOxnO90WnpPgPFU7toOZZIGr0k6ZMxeggx2O7SHnu6vvM7vCrJsat+n8jNaPkJazjkRhRTOLtnf5kWe2yTmQ/zeLuW8MaIsO2KlLbmQv6L+E8pfItbld+9H18/cBADb0yuHFPbBbmMIo5CwgxEtG1Yjyn1fmev92QDp1VVoBuqGuzPSwlKPu4bzsbLMj6PYusXnncjpp1BnGxvpwGfRMGAYM34q+USVTSuRY/uPHoWQuyu6ulAM7DcYirf1CfaHpgJENbwSJnJsSoXXz9706gZl0eyzvA8bNeTqfM7IpbVNXSbJiQjngJNUA5MWy1yQVZkfMOhUbCCrHlHbdw4vkdiVSaayQKWUcd7pccz1JjdQ3mC71/qUZSPgbvnOpXLxXoSxqzH6MM0xZY65m9x3WJaXY+x7ppFD9B7f5S6evoO1DNzCwsLimuKZMvCCT3bwSN7VxdYUk9kRgH8a6jMtZjQLk7n0Kwms7iuAfZoM4tXZAQCgJbteT67mq0kL+TnXpPAdOSKAz70YS3fqF5BckAEt97lqNx/wNyVRwW4MqG2T7X9S9quHMz4nLFvoT7cUSjPUx1afzCO2/BBptZXdY67k0+1EFI37ZEHfHmX9Hm9wxZ+KBW345wg3pM/WriZX5gl3wyfLyGRZt+PLMVI+dxZv51jvaIeydcasg9Oroifb5HBEuQsjlOlmlI01n7vALr9LDckUQnvUF5YVencht/mPb8W/aHMbXz9GP4IbsnLo6jzkKArXodxlIo9gpYBKCsGwmN/DMiW73KQysigI1UKn/MENWQhMq1idKdRCigzHKDRoOuC73VASMl5BbMS6jGas306Ez+/dfx8vhBQUS+Fu8w5ZafhCLvkywglFUghusz6hKyOQNdFK8v2RPnWpm46LWJ5s0zgsf8U9YF0VrGkc5DGI8L70++xPgyh/i4A70LRHK4tIdIBuVK7lp7KOuk15zFfccY7ODOYKnVo0lHdEquCwEo64L+fx2bfYN/YMnzOviJ1PZSnkknXO62c4FisvPlzfYmlbySrmE9ZxL9TFTP0Ub8q6bIvtZ2Tb7YUM+gr2Fulxd7V3g3JsxmW/neRYKzpdFC7Zpp+LsB8VlHc2u8dxUFqeYHSicBWykb8bZnk68su4zE/hj7Srb/O8aWvJ3x5usKN9Ugkd8jt3EU6QrWerT8+J+Uwn8KRHQcyy3G5UR1HcjV51alY46LLht7QlbVaScEKK2rXDbc5pn5XLOBTkhbL3ZJMFLOXZub3iYJ0oulrL49a7HB6iLzPEuOH2ayus53T43Df8Ie6MeV//yvTH5zXLEq/J/R47zu+P+0j0+a7BTn9tmQxkGvjiBmfwZi+GsKLDvZlmOfeSBwCAiMfnO24c0yk73krb+2hUZn8rDoZkjd/H8nVUFeuichWpMcoYK3MtlkN3G468H3dv8KBydcrnOXFFHFzMUepfZbFR+rEJZyLf5cgox6liCA4LmCpaYjqyfoznoazJnBE7eKaQhJFHI5RmLpzgIVP0kDJK7F7ickXV3AtFlid+n23lKF3WbytG9Wrk4raSGAcygWwq422ycaWmqMGcSn20q0P3iWKPSz00yDkIqjyAKwfSS+kwOaQEwp6ySQWjPgpTLrqr4fqqNgAYDLgQ35JTU9Nx8DGV6VSmpm5CDjxzmr0242NcGGWXUpQ+V+TjPWUuSooEJKNF4B0+p+Ow/NMjqvamKTZKIhzGXEmsw3EuBC2l/6o1lHzbTLGvBaVR4DjJGt6zEsE4LHHhzI/3sboggTqW2mcdhJU+by/FcR9LvojPNKgqrNzl+A6POZfMAxoZxMZ5dFNsN3yM8jruU00S6cr2UJ6aycBHW5mC9kRAb4BzyZnimAcXewhCOthWGr7zEElSVzFX7nmH6Ms7s7fibxmZg94u8O/LHBeaveg50orv1Lt1+6n1tyoUCwsLi2uKZ8rAa31lROmTKSVKC5QVNXBguDrFlSGlneaKWFr0sdRKCm39TZhbkROpQm5HlSR01kMhoZXP0JwrcpWbb8oIfKHQDoZJXgORphl0gCcWc6scxllV0ep8MtDcTbm3N1i+IQkgXjFLvP0O2Unv6OkJSL8clkuuwu/63D4GqzQ2S3yXp732+AOu4rEd1vPibI6oQlTcKPOgxplLpaDsQC1oB7EEIg7f0VQ847xMxFazK5mfwrmnRL0hMpNl/mU+t6qY5MscWkuye1eRGRvbWv+rfOcHcnzJbxwiN1Mkwb3J2jLJykztLMzn3/bfQm/G8nhlfibibMOOMvLMJwYvKt63J4eV9oacWXw+5/XHZDVbRRfTGOXl9OUkph1DUFLsdL8MV6qBrQuy89pjtn1XB6r5whLLHvvyyMiNXDu9bItmoaEk3x1MXfS1o0gUa2vLBABie6zXg0OW52ZkgXM542SUcHcWkM3elzlkbLVAM6ydVl/x+JUlaTWj7GY6RDx0G/CUsNnMWddFmp8VVzkxO0uMFXbgVHHhQ+dUrwRS+c3ibcwUdyVvWNbpVZx/5XlNKHaMk97EhlQbfS+6tkzO83xnvqc4J2EP8U3uNLtSvXYNVa+xSzZoaGuMlBzWLi44xrLacUSVFTqyYD+oNV2EimT5zcfK2rPFuaH/PvvBbqmDpSJGjPz7AIC4TCrRV76D+KuIFFiOO1XKZjBTuAVFJM2DsrnwcxgrebobeXosFMvALSwsLK4pnikDT1xQv3TsygQu2EG3Sr1pWhH3fI86qdgRme8oWsHtiuJtK5flakzG7HqK6jXmylVp+1im+I5QTYcoO6ziUVDRtQ24IWX2qSqbypWOPU1mOkQETojlCUvPlW5yVT8v8vNAesPT4z6yK7lab5u1ZZJXfsLWggdJL5VdzCvUgcc+zc8eqB8fJViWRDmBxpRldR5zDc4sKa9LxRZOrfj9R1Z7eD+hDOMvkHlNx/wt26Pc/FARi8/xEPPsjpxxlFHGD+mQbG+JkA493QMFiepRpitd6ymGujt2kCzpwGa0fhdrNpUTVCeg524FB0UF2crQhG3V5rsOtI36nAnD6BAzLbfqkPTS2nCgoOxPi+wUYzmMpcVUXZCNhqfU1RaTBtGFMrjI/NDZUdYkZUq60dvGeYyRCRPvSKe+SSedwKesaxOWab8Uw2CgYGyLp+c5/Epo/yb7e32P7HXX9zHRmPC2FUXvPndJZ3JP32weI5XkTiumAGg9ObvdUuCut3Vwm+vmMGKXQHPF37YHZIBv6UA4HkzguNpByEGqdo8sOCQHsXCyiNqA75pFeKZS9NlukS77xeOqDttTQDNP0+Flo7e2TLYyMv9UFMB0oYXs77Mc9RTbaUfnRd34Vbz+JKAcAzHNF8kQ5WhylM3ZGzo3KvaQCPEd5V2y4ulcprY60zurLjBvaGcR47WpEZ93qN1NudRBJMp5oiOtQMZovHi8ZlMZnzZ2dhBXAL9R8PRgcJaBW1hYWFxTPFMGHt1W1ooE2V72dIHqx8mWSk2ubmd5MtsXXJkxbfhwlVWnnlas5SxX8cKAjPBCWbXdzRYaA16bFkNugdc6U+oGc41b8L6NZliz+8rKXuC7c2Hq/xx3iW6L/090qdtsbFK3lcqQUZxckplMJgVElLdu/GC8tkz6cWWPUd7CYNKBmVBnPb6tcLfSJ6cPpat3Sti7JAu40CF1ZkSlfDZGduSJdb01nsPz6Sw1DPGenaV0lQnKaBW6iXyJzPH+gKF1K2nFQxZ7D9pxRD3q8IoLMu9QQH15QTk8gxkZxCjqoaUMQ8iv77RyI0drjfoJrQmQ7qHaZXkiVb6rpnif/SSfH2934Ihhye8LE/UhN+D3D8TIP3aZgyMWlaxQBs1jMqdcUSEKjmvYPiCrevCBLJqUEclc8NrPhh9gb0bZ9nb5PP+Q+mBXgdbc9xUzfXKI7S3K1gnWDzsMAGcz7kyKCrf70DhY5ciCTZXMMVzQ4UiH8jexBJpQtvMN7TCr3KUNFFQpH74yRXQRblK+kTDHy0mFfSWh/lbfTCG1+iQAIBRR9qwar63maVYX6xv4Rf5W1u6xMZI1F7jjLlf47jfbQxRc7oB7/vo2pxHljlykOTYuD12Et/ldSoYhXe3YVzsHAIBR30f4Ktfk++zLu/wJbz2UGeAe5ToYbiGc5HfnQ7ZbXKw/n1Io6HYXNe3ulqeU9YOEzpty2t0Pt+C5nP9yGcrL0bjJZtQfNuXMl00gu+Tu4aTyqafW3zJwCwsLi2uKZ8rAp0syGe+cq1IlC5gjMpUP5F6ckOvue7cVUL67h/Mp2WlY9t4PZDfqS0caX5JtNBcuXpbTSluBiFrSQSVnvLa22YH/QHr2hDJsKKtHe8Fron2DqUe21XPIwvJxOTjUyNiCCMs0jNfhH/IdJvX0E+MvB1+B5Lcq/PTSM6T6ZC05Balf3iQDrzfIrjJo4Fzut7kp2cWoJ9n61O9H22Q4xeIEUGCr2EIhekuq54jPRWSA1eqHAAAH+2Sgq3Oyg7YC3GfnYyxCYjaysR/GWb6RbPmHykiUnIWQlp64uVrfCuVc7sfRLOsbOQthpQwz1TuyFtFupC5rB6+7QFoB8pMlyi9juCsZRJWrs09WWEcYqaUC70f43J5smPMe61YpzdB5UzkkM7w/I2eraYwJFSLpfYSX7MsZ6YjbeTmlKKxxN0tZD0sb6Cp35Pbu+v4CALB9i++YTNme2SCDgdTGps4xAVn9FNU/3nBryM85Fro+GeMNOdwYQ8Y88pSv0oTQynAs9ccys4rxb1fu39MwEJ1Q33/coswyu6zXjS7b4hQ1zFusYxvMl5oCz5RqV1mgpmS++WQa/c+TKheS67vSTxXEKibfBGQ9tM4pg55MQwoVjt2pQsj2VxnoyAODF9jfwwu2tS/rpgtZiCDqoqBw1ZUod6CLKfvZ3CFLPx6m4LW0U19xDOSUo7ZYpjVctn6JwUS5R7VDeEkB8xYK77wf4zhq99toKd/o3sSGk7WwsLD4psQzZeCzDFf88IIn0o0JMJFeKF3gCpoQuytdilmmoki7XA0vT7nadrI8+d/rUB/dlYt48tLDRY465ZpHK4CiAkBdKNVadAgMlRV6NuS7g0v+HUsqV+dJAo7L1TKTlF5uqhPshEKwNmk10nbegZPlir+43FxbJnm5qvevqNR8A1CYz7ZPe9viJXXCzpDs82F8hIzS0rWUmOAqN2Qsr9Rq8nhbjsMIa4cyVuArpMhIGobMq3QewNzk+/OP+Ny6S/ayLVv0gVfE3gbZqpdRkgzFHogqX2JBFhfBoIypwgQP5+tnpd9RyIRjeX7WghGiMbb9Rpv1fWeDbb6vswNnNUAHtOgZKDlG6PQIAJDQRuODHnWN6VIXsY6sKppkTotN/uZJN7o7CeMtfJbfLakznju8p5xi2wchH06U9yWk164MaA10HJNtb1g7mpSLWIwymczXT/wBAEOfBx6ZMNlswyQAsbqM4fuSVcp72GB/KO9kMe1RDvGszkW0o43nqa+HUnTuZ110ljprCIulX7IfTWSN0gpPEFd4CldBzY4esf/3QeuyaWyKxJj63NH/196ZNLdtBFG4CQIESYgiTUo0acmSbNmxXUmqnL+Q/PscckicxfESKVrMVVxAiAtIIIf3eHUVc3AKqfddVKoiQcxMT093z3RPHTLsNc/MzOwxz11/oCx7tcTiAPsI08nueRSlFfp9ts8Y++QP26P1WmVxtRKvxls5mAeFvdi8EuZ1K4c2XPIIerCEjgrpebbjjd3T8w096JsGyyXMR5DTF3sru/QgZM95TV1Ib7UQYRze13w7Ytld1+E+Be919T3ouuGaJ7e8vL1wuO9S+3x5ji+qwCsfMUAha9w2Np7lXQx6eQYhqMRMjAjQgAfziXV4E0/Izii1sBnUKUNo0wEP2G9iu8a+pj0fYPDGAQYodTGxOvsbO7pAR3onDFEkENLJW3RHK17Y7/RcVvsMtxhGeHyBdyi6EP6Tbmp91vU9rO7uGrcd1nzJ43c2QWq/sH535Y4hixb6aHTO5KZuZEMeb5yN0M7D0oWZmQ1LmIDlBSZydelb7xTHtOwOQvtyzTsii5hkb48DezrD93sP4JTNKmdmZubcUgG5B3aUh4CF96wFMcH/K48hC262Jvl3Vi5gIUn83Td2u9z4bLDudtPxbe1h4s8DaJtXDhewLtoyyUVWd5HUNB1h8XHZp1ef0BeH3MwMb0O74T2ETSaMnXcRFnDpdneXVTs6QRgpiNknayx4c9a4vr907LSAz3+qo53zKX4jP4ficngLjw1XVsuxuuTxzl2C77Euz/UG8vZt60dbsIri/H5bsxwy02MopeiWbEmjZquEuwylPOeC5vDS47vcwpI9yNM1wwRtKrYc3f7z5cRi3g87ibGQpAEmXcIaL5PLpTkPIRuRg3kyY2kG/wDKallFHzber239AO+1aOy+iZmySmh0jvZ+PapZ5w0rRG4T9XjsMl5CPqLuxJw23nkd47dPCgxfVFl5sohn1NvPbPUzZCyo8r5MGqJ5Jj05acFerND4mMdST4uQh/cMxZzFL610jE3ogDVkoiXG5Yi3+Gym6JOWl7fSAvKYfFQIRQgh/pd8UQu8XIF18mibKu1d2TEvruu6sCTeulilTjxYjcPpzIrcWBqzuNOTHquq0b0fhFjVV0nRbIyV66chnuu0mOLMhJJFFNo6wKq4R5ftkAfo/QJWxr47tUaKFfrTBtZlyiNOIYtHJayO5zXrFhTx2fH8duc+udnAAszX4AovZnlrbpOa2IZlF3/LLqsyxnPr52EF1MZwC++Y5u7wJpDRAM/90Ert/B7ucL6JPh6vYVlub5OfFIfWKWJs0gld/4jty8EiW+9X7a8NCxrxtp6Ci/5avYMdcHvO9OtRarUyLK/O1e71wL/hV5wSNtJ+uxvYYRMWXfsCsvA3a5zXXuN9O/eJ1feZzDHmrfFlejBMYZ+yz56se+YnDMmUmSbfQHhi3mBCRtg3S5js8xiucu4Nxrn6Hdp24Z/ZYlvHfYRwS+0Z+ubRGEftpgHCOr3w0BwWwTpo7R5WMjNbV3D89ZhJTH+OKnbEZDTfg+z92oEX2Kpg/HquZ4nPaoQDlkt4zOO3K9Zd5/HC3ChvB114UWkLbb7hXl7wBv0U7RdszoSp/gLWr7/Ab3aYDLNyfVtyTs4HjM/EmNdRlynrXo+fPbXoiuHJyu6yknsKWX51g2fcJIk9fM3jpEM876sUctBmNc+Lct5qrA2+bMMrKq4hR0UXHtVehEhAu5xa9D3mwLJOLyeFLPohvtOv1+zhiHPWhd7qTfHZH1g10yvmLOnhDlq/gfersL54v4ZxqTXxO74XWTWBdZ6rfL7sgixwIYTIKLk0/XfWgBBCiP8WWeBCCJFRpMCFECKjSIELIURGkQIXQoiMIgUuhBAZRQpcCCEyihS4EEJkFClwIYTIKFLgQgiRUaTAhRAio0iBCyFERpECF0KIjCIFLoQQGUUKXAghMooUuBBCZBQpcCGEyChS4EIIkVGkwIUQIqNIgQshREaRAhdCiIwiBS6EEBlFClwIITKKFLgQQmSUfwAifviz98tsIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
